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공학박사 학위논문

Development of a Sonic Branding Process

for Designing Automobile Engine Sound

Based on Affective Engineering Approach

감성공학적 접근법에 기반한 자동차 엔진음의

소닉 브랜딩 프로세스 개발

2018년 8월

서울대학교 대학원

산업·조선공학부 인간공학 전공

문 소 연




지도 교수 윤 명 환

이 논문을 공학박사 학위논문으로 제출함

2018년 8월

서울대학교 대학원

산업·조선공학부 인간공학 전공

문 소 연

문소연의 공학박사 학위논문을 인준함

2018년 8월

위 원 장 박 용 태 (인)

부위원장 윤 명 환 (인)

위 원 박 우 진 (인)

위 원 류 태 범 (인)

위 원 이 상 원 (인)

Abstract




Soyoun Moon

Department of Industrial Engineering

The Graduate School Seoul National University

Sonic branding is an auditory method by which brand identity can be

presented. There are previous studies to group car sounds into three

categories, namely electronic sound, engine sound, and operation sound, as

an auditory user interface (AUI) and studies are conducted to apply sonic

branding to all AUIs in cars. In this context, car makers have put effort to

make their brand images appealing to customers using engine sound

achieved by hardware improvement, including intake and exhaust systems,

for decades.

i

Car makers are trying to represent their brand identities using active

sound design (ASD) systems, which are software solutions for engine sound

design that can be applied to current infotainment systems. Software

developing companies in Germany, France, and the UK have launched their

own ASD solutions, and the maturity of the software reached to a certain

level at which the solutions can be applied to mass production of cars.

Considering that the differences in hardware performances depending on the

brands are currently less than those before, the interest on ASD in the

automotive industry globally as a key method for improving affective

quality and presenting brand identity using engine sound, which is an

important AUI in automobiles, is increasing. However, most of previous

studies on ASD are for ASD algorithm development and there are few

previous studies on the ASD development process, which can be applied to a

real engine sound design. Moreover, it is known that car manufacturers have

difficulties in finding methods as to which brand identity can be presented

using the ASD system in their cars.

In this study, a sonic branding process for ASD is proposed, by which

brand identity and image is evaluated based on the acoustic parameters of

engine sounds, and to which a sonic branding method can be applied. The

overall development process has been built based on previous studies on the

AUI development process to keep its reliability. Subjective ratings,

interviews, and questionnaires, which are subjective measurement methods

for affective engineering, were used to propose methods for evaluating and

designing of engine sounds. A regression model for engine sound, which

shows the causal relationship between acoustic parameters and affective

ii

adjectives, was derived for efficiency in evaluation and design.

For this purpose, a sonic branding process, by which levels of affective

adjectives of original engine sounds were evaluated on the positioning map

and were adjusted to new target positions to improve brand identity using

regression equations for acoustic parameters and affective adjectives, was

proposed. The positioning map is a two-dimensional grid for engine sound

evaluation, which has been used in previous studies. Second, a jury test was

conducted to propose regression equations, which are required for the sonic

branding process, and the result of the jury test was analyzed using statistical

methods to find the causal relationship between affective adjectives, which

present brand identity and car class, and acoustic parameters of engine

sounds. Third, new affective synthesis methods, which can be applied to

engine sound design using ASD, were introduced. Synthesis methods,

including musical harmonic theory and formant filter, which are being tried

by car manufacturers currently, were described, and new methods including

pipe organ mixture, break back, and beat effect were proposed. Fourth, new

engine sounds were developed on the ASD system equipped in target cars

following the sonic branding process to test the validity of the process. The

engine sound design was conducted in cooperation with a sound design

expert, who has insights for sound design. Six engine sounds were designed

in total and the positions of five engine sounds were found in the range of

target positions. Even if the position of one engine sound design was out of

range of the target position, the direction of movement was still within the

intended trendline.

iii

The result of this study can be used to develop sonic branding for engine

sound. Development time can be decreased and the possibility of success

will be increased using the sonic branding process proposed in this study. In

addition, the regression model for engine sound developed in this study will

be helpful for estimating the character of new engine sounds for ASD

systems in advance. Estimating new engine sound characters will contribute

to proper engine sound designs to attain an efficient brand identity strategy

using engine sound.

However, the jury test was conducted for participants who have Korean

nationality and luxury and sporty car owners were excluded from the test. To

adapt the result of this study globally, additional study considering the

nationality and owned car type is necessary. In addition, sonic branding for

electric/hybrid vehicles based on the result of this study could be an

interesting topic for a future study.

Keywords : Sonic Branding, Affective Engineering, Active Sound

Design, Engine Sound Design, Sound Synthesis

Student Number : 2012-30285

iv

Contents

CHAPTER 1. INTRODUCTION ............................................................ 1

1.1 Research Background .......................................................... 1 1.2 Purpose of this Study ........................................................... 3 1.3 Organization of the Thesis ................................................... 6

CHAPTER 2. BACKGROUND ............................................................. 10

2.1 Engine Sound and Brand Identity ...................................... 10 2.2 Active Sound Control ...................................................... 14 2.3 ASD for Automobiles ...................................................... 17 2.4 ASD for Electric/Hybrid Vehicles .................................... 21

CHAPTER 3. SONIC BRANDING PROCESS .................................. 23

3.1 Sonic Branding Process ..................................................... 23 3.2 AUI Design Process ........................................................... 27 3.3 Stages in Sonic Branding Process ...................................... 33 3.4 Discussions ........................................................................ 55

CHAPTER 4. REGRESSION MODEL FOR ENGINE SOUND ....... 57

4.1 Regression Model .............................................................. 57 4.2 Subjective Measurement for Affect ................................... 59 4.3 Jury Test ............................................................................. 64 4.4 Descriptive Analysis .......................................................... 69 4.5 Correlation Analysis ........................................................... 71 4.6 Regression Analysis ........................................................... 75 4.7 Discussions ...................................................................... 82

v

CHAPTER 5. AFFECTIVE SYNTHESIS METHODS....................... 84

5.1 Synthesis Methods ............................................................. 84 5.2 Examples of Synthesis Methods ........................................ 85 5.3 Synthesis Methods for Engine Sound Design .................. 90 5.4 Proposals for New Synthesis Methods .............................. 93 5.5 Discussions ...................................................................... 103

CHAPTER 6. ENGINE SOUND DESIGN AND TEST ................... 104

6.1 Engine Sound Design and Test ........................................ 104 6.2 Target Vehicle and Engine Sound Samples ...................... 105 6.3 Defining Target Engine Sound ......................................... 107 6.4 Engine Sound Synthesis ................................................... 111 6.5 Test and Validation ........................................................... 122 6.6 Discussions ...................................................................... 135

CHAPTER 7. DISCUSSION AND CONCLUSION ........................ 137

7.1 Summary of Findings ....................................................... 137 7.2 Contribution of This Study .............................................. 145 7.3 Limitation and Future Work ............................................. 146

BIBLIOGRAPHY ................................................................................. 148

APPENDIX ............................................................................................ 166

ABSTRACT(KOREAN) ....................................................................... 175

vi

List of Tables

Table 1.1 Organization of the Thesis ........................................................... 9

Table 2.2 Opinion on Adding Sounds to EVs (Petiot et al., 2013) ............ 21 Table 3.1 Five Parts and 13 Stages of Sonic Branding Process ................ 35 Table 3.2 Comparison between Previous AUI Development Process and Sonic Branding Process Proposed in this Study ........................................ 38 Table 3.3 Example of Standardized Verbal Descriptions (Edworthy & Stanton, 2007) ......................................................................................... 53 Table 3.4 Example of Technical Descriptions of Warnings Described in Table 3.3 (Edworthy & Stanton, 2007) ..................................................... 54 Table 4.1 Subjective Measurement Methods for Affect (Helander & Khalid, 2017) .......................................................................................................... 60 Table 4.2 List of 38 Cars for Jury Test ...................................................... 65 Table 4.3 Result of Factor Analysis .......................................................... 67 Table 4.4 Result of Internal Reliability for Refined and Powerful ........... 67 Table 4.5 Additional Acoustic Parameters ................................................ 68

vii

Table 4.6 Result of Descriptive Statistics for Psychoacoustic Parameters/Acoustic Parameters and Affective Adjectives ....................... 69 Table 4.7 Correlation Analysis for Psychoacoustic Parameters ................ 72 Table 4.8 Correlation Analysis for Additional Acoustic Parameters ......... 73 Table 4.9 Additional Acoustic Parameters for Regression Analysis ......... 74 Table 4.10 Result of Regression Analysis for Refined Dimension and Psychoacoustic/Acoustic Parameters ........................................................ 76 Table 4.11 Result of Regression Analysis for Powerful Dimension and Psychoacoustic/Acoustic Parameters ........................................................ 76 Table 4.12 3 Groups for Engine Sound Characters ................................... 78 Table 5.1 Musical Harmonic Theory Analysis for 6-Cylinder Engine at 5880 RPM (Yun et al., 2012) .............................................................................. 86 Table 5.2 Frequency Rate for Intervals ..................................................... 91 Table 5.3 Pipe Organ Harmonics and Engine Orders ............................... 97 Table 5.4 Example of Engine Sound Design with Beat Effect in Low Frequency Band ....................................................................................... 101 Table 6.1 Ranges of Refined and Powerful Levels for Target Positions . 107 Table 6.2 Acoustic Parameters for Original Engine Sound ..................... 110

viii

Table 6.3 Range of Acoustic Parameters for Target Positions ................ 110 Table 6.4 Engine Sound Synthesis Methods for Target Engine Sounds . 111 Table 6.5 Intervals for 4-Cylinder Engine Sound Synthesis ................... 112 Table 6.6 Intervals for 6-Cylinder Engine Sound Synthesis ................... 114 Table 6.7 Application of Break Back Method (Engine Sound #1) .......... 116 Table 6.8 Settings for 2 Formant Filters .................................................. 118 Table 6.9 Result of Order Level(dB SPL) Measurement (4 Cylinders) .. 123 Table 6.10 Result of Order Level(dB SPL) Measurement (6 Cylinders).124

Table 6.11 Refined and Powerful Levels Calculated from Acoustic Parameter Measured from Synthesized Engine Sounds .......................... 129 Table 6.12 Example of the Operational Test for Overall Order Level (4 Cylinders) ................................................................................................ 132

Table 6.13 Standardized Verbal Descriptions for 8 Engine Sounds ........ 133 Table 6.14 Technical Descriptions of Engine Sounds ............................. 134

ix

List of Figures

Figure 2.1 Example of Brand Sound Design, Adapted from the Study by Penne (2004) and Gabriella (2009) ........................................................... 11 Figure 2.2 Porsche Sound Symposer (Colwell, 2012) .............................. 12 Figure 2.3 Music Score of Modified Version of Aria Nessun Dorma from Opera Turandot, which was Used for the Maserati Engine Sound Design (Ha, 2011) ........................................................................................................ 13 Figure 2.4 Active Control of a Plane Sinusoidal Sound Wave Propagating from Right to Left in a Duct: (a) Spatial Distributions of Pressure at One Instant Due to Primary Wave (Blue Line) and Secondary Source (Red Line); (b) Net Pressure Field Showing Destructive Interface to the Right of the Secondary Source, and a Standing Wave to the Left (Elliot & Nelson, 1990) ................................................................................................................... 14 Figure 2.5 Level of the Amplitude of the Sum of Two Sine Tones as a Function of Their Amplitude and Phase Difference ................................ 15

Figure 2.6 FXLMS Algorithm (Kim, Park, Ryu, & Lee, 2015) .............. 16

Figure 2.7 Overview of ASD System in BMW F10 M5 (http://www.bimmerpost.com) .................................................................. 17 Figure 2.8 Sketch of a Typical ANC System for Interior Engine Noise Reduction of a Car Consisting of an Engine RPM Sensor, Speakers, Monitoring Microphones and an Adaptive Signal Processing Unit

x

(Schirmacher, 2002) .................................................................................. 19 Figure 2.9 Sketch of a Typical ASD System for Interior Engine Noise Reduction of a Car Consisting of an ANC System Plus Explicit Target Signal Definition Process (Schirmacher, 2002) ................................................... 19

Figure 2.10 AVN App Screen Layouts (Park et al., 2015) ........................ 20

Figure 2.11 What Should an Electric Vehicle Sound Like? (Petiot et al., 2013) .......................................................................................................... 22

Figure 3.1 Sonic Branding Process and Related Chapters ........................ 26

Figure 3.2 Categories of the Sounds Presented in a Car, Modified from Original Version (Park, 2013) ................................................................. 27 Figure 3.3 ISO Procedure for the Development of Public Information Symbols (Easterby and Zwaga, 1984) ....................................................... 29 Figure 3.4 Diagrammatic Representation of Design Procedure (Edworthy & Stanton, 2007) ........................................................................................... 30 Figure 3.5. Cognitive Engineering Design Process for Natural Warning Sounds and Auditory Alerting Systems (Ulfvengren, 2007) ..................... 31 Figure 3.6 Research Process for Target Sound Development (Kim et al., 2013) .......................................................................................................... 32 Figure 3.7 Block Diagram of Sonic Branding Process for ASD ............... 37

xi

Figure 3.8 Block Diagram for Part 1 : Regression Model for Engine Sound of Sonic Branding Process ...................................................................... 39

Figure 3.9 Expert Powertrain Sound Characterization (Abe et al., 2004)…41 Figure 3.10 Block Diagram for Part 2 : Brand and Synthesis Method of Sonic Branding Process ............................................................................. 44 Figure 3.11 Block Diagram for Part 3 : Engine Sound Design of Sonic Branding Process ....................................................................................... 46 Figure 3.12 Block Diagram for Part 4 : Test and Validation of Sonic Branding Process ....................................................................................... 48 Figure 3.13 Target Profile and Before/After ASE (Lee et al., 2016) ......... 49 Figure 3.14 Target Positioning and Position of Prototype Engine Sound ................................................................................................................... 51 Figure 3.15 Block Diagram for Part 5 : Verbal Description of Sonic Branding Process ....................................................................................... 53 Figure 4.1 Example of Regression Model for Engine Sound ................... 57

Figure 4.2 Magnitude Estimation of Pleasantness and Powerfulness Dependent on the Experimental EOLE increase (Bisping, 1997) ............. 62 Figure 4.3 Judgement for Classic Cars by Classic Car Owners ................ 63 Figure 4.4 Regression Equation for Refined ............................................. 77

xii

Figure 4.5 Additional Regression Equation for Refined ........................... 77 Figure 4.6 Regression Equation for Powerful ........................................... 78 Figure 4.7 Positions for 3 Engine Sound Groups ...................................... 79 Figure 4.8 Range of Acoustic Parameters by Group ................................. 81 Figure 5.1 Comparison between Engine Orders and Musical Intervals (Nobert et al., 2003) .................................................................................. 85 Figure 5.2 Process of Target Sound Synthesis Based on Formant Filter (Chang & Park, 2016) ............................................................................... 87 Figure 5.3 FFT Analysis of a Tiger’s Cry for Estimating a Formant Filter (Chang & Park, 2016) ............................................................................... 87 Figure 5.4 Time Functions and Associated Frequency Spectra of Stimuli Commonly used in Psychoacoustics (Zwicker & Fastle, 1999) ................ 88 Figure 5.5 FFT Analysis of Synthesized Engine Sound with Different k Values (Kim et al., 2017) ........................................................................... 89 Figure 5.6 A Part of Music Score of Modified Version of Aria Nessun Dorma from Opera Turandot, which is Used for Maserati Engine Sound Design (Ha, 2011) ........................................................................................................ 90 Figure 5.7 FFT Analysis for Formant Frequency Estimation for Tiger’s Roaring Sound Sample .............................................................................. 92

xiii

Figure 5.8 Pipes of Pipe Organ ................................................................. 93 Figure 5.9 Harmonic Structure of Clarinet and Flute (Dickens, France, Smith, & Wolfe, 2007) .......................................................................................... 94 Figure 5.10 FFT Analysis of Pipe Organ Mixture (Richard Strauss – Zaratustra Intro) ......................................................................................... 95 Figure 5.11 Music Note for Organ Mixture (Richard Strauss – Zaratustra Intro) .......................................................................................................... 96 Figure 5.12 Example of Mixture Break Back Based on the Interval Perfect 4th and Perfect 5th (https://en.wikipedia.org/wiki/Mixture_(music)) ....... 98 Figure 5.13 Concept of Organ Break Back ............................................. 99

Figure 5.14 Beat Effect (https://en.wikipedia.org/wiki/Beat_(acoustics))100

Figure 5.15 Temporal Envelope for Recording Samples of (a) Original Engine Sound and (b) Synthesized Engine Sound using Beat ................ 101

Figure 5.16 FFT Analysis of Engine Sound with Engine Sound Design in Table 5.4 .................................................................................................. 102 Figure 6.1 Wave Form of Recoding Samples for Target Cars with 4- Cylinder and 6-Cylinder Engine ............................................................. 106

Figure 6.2 Position Proposed for Target Engine Sound .......................... 108

Figure 6.3 FFT Analysis of Engine Sound #1 (6 Cylinders) ................... 116

xiv

Figure 6.4 Formant Filters for Sound #3 Synthesis ................................ 117

Figure 6.5 FFT Analysis for Engine Sound #3 (6 Cylinders) ................. 119 Figure 6.6 FFT Analysis (Water Fall) of Original Engine Sound and Synthesized Engine Sounds (4 Cylinder) ................................................ 120 Figure 6.7 FFT Analysis (Water Fall) of Original Engine Sound and Synthesized Engine Sounds (6 Cylinder) ................................................ 121

Figure 6.8 Result of Order Level Comparison (4 Cylinders) .................. 127 Figure 6.9 Result of Order Level Comparison (6 Cylinders) .................. 128 Figure 6.10 Result of Position Matching Test ................................................................................................................. 130

xv

CHAPTER 1. INTRODUCTION

1


1.1 Research Background

The results from previous studies show that building strong brand identity

is an efficient differentiation strategy (Aaker, 1991; Kleine, Kleine & Kernan,

1993; Belk, 1988; Malhotra, 1998; Rhiu, Kwon, Yun & Park, 2016). For

decades, car makers have put efforts to make their brand images appealing to

customers using engine sound improvement hardware including intake and

exhaust systems (Bodden & Belschner, 2014). Porsche is famous for its

distinctive engine sound called Porsche Note. Audi put its efforts to design its

own engine sound, which is developed for brand identity. The engine sound

of Audi is called Audi Score by Audi engine sound lovers.

However, engine sound development by changing hardware requires much

resources and it is difficult to modify the hardware again when the result is

different from expectation. As an alternative, active sound design (ASD) is

gaining momentum. Currently, studies on ASD and development of ASD

algorithm are led by three companies, i.e., Muller-BBM (in Germany),

Genesis (in France), and Neosonic (in the UK). Some cars equipped with

ASD systems are beginning to be launched in the market.

ASD is a virtual engine sound synthesis system composed of additional

software and the existing audio system, which produces artificial engine

sound through speakers inside a car. The sound from the speakers is mixed

1


2

with the original engine sound, which can be heard inside the car.

Considering that the differences in hardware performances depending on the

brands are currently less than those before, the interest on ASD, as a key

method for improving affective quality to customers, is increasing in the

automotive industry globally.

There are few previous studies on the engine sound development process

for ASD, which can be applied to a real engine sound design. Recently, some

studies on engine sound design using ASD systems were conducted by ASD

algorithm providers and car makers. However, most of the topics on the

studies are on introducing their systems or case studies for a specific engine

sound design. An efficient and reliable engine sound development process for

ASD should be studied as the number of cars, in which ASD systems are

equipped, are increasing globally. In addition, studies on the relationship

between acoustic parameters of engine sound equipped with ASD systems

and perceived brand image for engine sound are necessary for the engine

sound development process using ASD systems. Furthermore, most of

previous studies were focused on engine sound improvement using hardware

adjustment methods or providing separate topics about acoustics parameters

and semantic tests for engine sound (Bisping, Giehl, & Vogt, 1997; Bodden,

Heinrichs, & Linow, 1998; Kuwano & Namba, 2001; Petiot, Kristensen,

Maier, 2013; Rhiu, et al., 2016). Therefore, these previous studies have

limitations to be adapted to a real engine sound development process with

ASD.

2


3

1.2 Purpose of this Study

In this study, a sonic branding process is proposed, by which engine

sounds can be developed efficiently for a target brand identity and image of

car class using acoustic parameters on engine sounds. Subjective ratings,

interviews, and questionnaires, which are subjective measurement methods

for affective engineering (Helander & Khalid, 2006; Kim, 2017), were used

for evaluation and design of the process. The relationships between affective

adjectives for brand identity and acoustical parameters of engine sound were

analyzed, and the result of the statistical analysis was applied to the process

based on previous studies for auditory user interface (AUI) design because

engine sound is one of the important AUIs in automobiles (Park, 2013; Jeon,

Bazilinskyy, Hammerschmidt, Hermann, Landry & Wolf, 2015).

First, a sonic branding process was proposed for engine sound

development with the ASD system, as presented in Chapter 3, which can

adjust the sound characters to improve brand identity. Previous studies on the

AUI design process were adapted to develop the process and obtain an

efficient and reliable engine sound design, considering that engine sound is

one of the AUIs that can be heard in a car. The properties of the ASD system

were considered for developing the stages, especially those that are related to

the engine sound test. Generally, the process is composed of five parts,

namely regression model for engine sound, brand and synthesis method,

engine sound design, test and validation, and verbal description. Each part

consists of several stages. Totally, there are 13 stages in the sonic branding

process.

3


4

Second, a jury test was performed for rating of engine sound characters.

Then, a statistical analysis was conducted for affective adjectives related to

engine sound characters and acoustical parameters on engine sound for the

regression equation. Such equation can be applied to the regression model for

engine sound, which is the first part of the sonic branding process. Two

representative adjectives were extracted from a factor analysis of a two-

dimensional positioning map, which has been used for engine sound

evaluation in previous studies (Bisping, 1997; Penne, 2004; Gabriella, 2009;

Bodden & Belschner, 2014). Then, the final acoustic parameters were chosen

from correlation and regression analyses. The regression model for engine

sound by which brand identity and car class can be represented, was derived

based on the statistical analysis result. Moreover, the ranges of parameters for

engine sounds with different characters were described as reference values.

Third, affective engine sound synthesis methods, which can be applied to

ASD technology, were proposed. Recently, car makers are trying to apply the

musical harmonic theory to engine sound development. Some car makers are

trying to adapt the formant filter, which has been used for engine phonology

or voice recognition, to engine sound synthesis. Acoustical parameters or the

playing method of pipe organs are proposed for the engine sound design in

this study, as the harmonic structure of a pipe organ is similar in structure to

the engine order. Another new topic for engine sound synthesis is engine

sound level linearization, which makes a linear increase in engine sound level

depending on the RPM. More details about sound synthesis methods are

described in Chapter 5.

4


5

Fourth, the sonic branding process was applied to a real ASD system to test

the feasibility of the process, as discussed in Chapter 6. The parts, i.e., engine

sound design, engine sound tests, and verbal description, are explained in this

chapter. The target position of affective adjectives was defined on the

positioning map, and by using a regression model for engine sound, target

ranges for design variables, which provided the target position, were obtained.

Then, order levels were extracted from a simulation software tool considering

the synthesis methods. The final order levels were applied to the ASD system

in a car and a new engine sound was synthesized by the ASD system.

To find the positioning of synthesized engine sound, acoustic parameters of

new engine sounds were measured and new positions were compared with

the target position. Then new engine sounds were applied to the ASD system

in target cars for operational test and verbal description.

The result shows that a new engine sound can be efficiently developed for

a target brand identity and image of car class using the sonic branding

process proposed in this study. An efficient and reliable engine sound design

will also be achieved by following the process. In addition, the engine sound

development process and regression model for engine sounds with ASD,

which are proposed in this study, will contribute to developing appropriate

engine sounds that fit each vehicle’s profile by producing new engine sounds

and building a brand strategy.

5


6

1.3 Organization of the Thesis

Chapter 1 describes the research background of the study. The history of

engine sound development in the automotive industry, problem definitions of

previous engine sound development methods, and limitations of previous

studies are presented in this chapter. Then, the purpose of the study and its

expected contributions are introduced. In addition, the overall methodologies

used in this study are presented with information about related chapters. The

organization of the thesis is also described with a figure showing the structure

of the study.

Chapter 2 introduces previous studies related to engine sound

development for brand identity in automobiles and engine sound

development examples of car manufacturers. The principles of active sound

control, examples of current ASD systems including an example of an ASD

algorithm are presented. A previous research on engine sound for

electric/hybrid engine is introduced together with survey results.

Chapter 3 explains the proposed sonic branding process, by which engine

sounds can be developed for a target brand identity and image of car class.

Previous studies on AUI development are introduced considering that engine

sound is one of the AUIs in the automotive industry. In the sonic branding

process, there are five parts that consist of 13 stages. Each stage, which is

built based on previous studies for an efficient and reliable process of engine

sound development, is presented in this chapter. More details on the five parts

that comprise the process are introduced in the following chapters.

6


7

Chapter 4 presents the regression model for engine sound, which is the

regression equation developed for engine sound design. Adjectives were

obtained from previous studies on engine sound evaluation and two

representative adjectives are derived from a factor analysis. Then correlation

and regression analyses were conducted to determine the relationship

between the two main affective words and acoustic parameters. Two

regression equations for two representative adjectives, which will be used for

position calculation of engine sound on the map with two adjective axes, are

derived.

Chapter 5 describes the affective synthesis method, which can be applied

to engine sound design based on its brand, for the ASD system. Current

technologies for engine sound synthesis including musical harmonic theory

and formant filter method are introduced. Furthermore, new synthesis

methods, which are proposed in this study, namely pipe organ mixture, break

back, and beat effect, are introduced. Examples of each method are presented

in this chapter for the engine sound design and test discussed in Chapter 6.

Beat effect was used for the engine sound design of a test car with a 4-

cylinder engine and its effect was tested only as described in this chapter.

However, it was not applied to the engine sound design and test as presented

in Chapter 6 because of its powerful effect.

Chapter 6 presents the engine sound design and test for two target cars,

i.e., with 4-cylinder and 6-cylinder engines, following the sonic branding

process proposed in Chapter 3 to test the validity of the process. The target

7


8

position of engine sound design was defined on the positioning map in

advance and three engine sounds for each target car are developed with three

different concepts using different synthesis methods.

Then, the final engine sound designs were applied to the ASD systems in

the target cars. The functionality of the ASD system and positioning

matching with final engine sound designs were tested at the related stages.

The operational test was performed with wide open throttle (WOT) condition

in the two target cars, where the ASD systems were equipped with three

engine sound designs. A verbal description for each engine sound design was

performed during the last stage of the process.

Chapter 7 describes the summary of findings in this study and presents a

discussion on the implication of the findings through the results of the study.

The limitations of the study are presented in this chapter and suggestions for

future research, based on the findings and limitations, are provided in the end.

Table 1.1 shows the overall organization of the thesis in this study.

8


9

Tab

le 1

.1 O

rgan

izat

ion

of

the

Th

esis

9

CHAPTER 2. BACKGROUND

10


2.1 Engine Sound and Brand Identity

The results from previous studies show that building a strong brand

identity is an efficient differentiation strategy (Aaker, 1991; Kleine et al.,

1993; Belk, 1988; Malhotra, 1998; Rhiu et al., 2016). In this context, research

for developing the brand image and improving affective quality using vehicle

sound is necessary. Car makers have put their effort to make their brand

images appealing to customers with engine sounds, which have been

improved by hardware modifications for decades (Bodden & Belschner,

2014).

The powertrain-tuned sound can thus be used to enhance the perceived

sound quality and performance and match the desired brand characteristics

(Ishii, Iwai, Ito, & Yamashita, 2003; Blommer, Amman, & Otto, 1997).

Harley-Davidson is famous for its unique motor bike engine sound, which is

the brand identity of the company (Aaker, 1997). Most of the customers who

purchase the Harley-Davidson motor bike enjoy this engine sound while they

drive.

Audi puts its effort to design its own engine sound called Audi Score,

which was developed for its brand sound identity. Audi Studio is a special

research organization that manages all sounds for Audi cars. The engineers of

Audi Studio record all sounds from Audi cars and analyze them to evaluate if

10


11

they are aligned with Audi brand (www. audi.com).

BMW has a strategy to build its brand identity with strong engine sound

characters designed by its own development process. Comfortable and sporty

are two main characters by which BMW build its brand identity in its engine

sound development process. For this, the levels of four characters, namely

loud, aggressive, silent, and characterless, are measured. Figure 2.1 shows an

example of a brand sound design (Penne, 2004).

Figure 2.1 Example of Brand Sound Design, Adapted from the Study by Penne (2004) and Gabriella (2009)

Porsche 911 is famous for its specific engine sound, the so-called Porsche

Note. To build this sound identity, dedicated hardware for engine sound

enhancement is provided. Figure 2.2 shows the Porsche Sound Symposer,

which is the dedicated hardware for engine sound enhancement.

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12

Figure 2.2 Porsche Sound Symposer (Colwell, 2012)

Maserati is known to collaborate with music composers when they design

the engine sound to make Maserati Engine Note (www.maserati.com), which

is a unique engine sound with a strong character. Even if not all people prefer

the engine sound of Maserati, Maserati Engine Note is still the trademark of

Maserati brand. Figure 2.3 shows the music score of the modified version of

the aria Nessun Dorma from the opera Turandot, which is known to be used

for the Maserati engine sound design. The score shows notes with various

pitches.

There are several harmonics that cause tension or resolution. These

musical factors can be applied to the engine sound of Maserati.

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13

Figure 2.3 Music Score of Modified Version of the Aria Nessun Dorma from the Opera Turandot, which was Used for the Maserati Engine Sound Design (Ha, 2011)

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14

2.2 Active Sound Control

Active sound control methods have been introduced In previous studies for

controlling the level of sound. By using these methods, the level of specific

frequency bands can be controlled by phase interference, which is caused by

frequency differences between the original and secondary signals, as shown

in Figure 2.4.

Figure 2.4 Active Control of a Plane Sinusoidal Sound Wave Propagating from Right to Left in a Duct: (a) Spatial Distributions of Pressure at One Instant Due to Primary Wave (Blue Line) and Secondary Source (Red Line); (b) Net Pressure Field Showing Destructive Interface to the Right of the Secondary Source, and a Standing Wave to the Left (Elliot & Nelson, 1990) Figure 2.5 shows the levels of amplitude of the sum of two sine tones, which

have different phases. The levels of amplitudes are different depending on the

phase differences of two sine tones as shown in Figure 2.5.

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15

Figure 2.5 Level of Amplitude of the Sum of Two Sine Tones as a Function of Their Amplitude and Phase Difference

Active noise canceling (ANC) is one of the well-known technologies for

reducing the level of noise using a reverse phase signal from an original one.

The filtered-X least mean square (FXLMS) algorithm is widely used for

ANC. Figure 2.6 shows an example of an FXLMS algorithm.

x(n) has a sine wave of engine order divided into two and one of these

signals goes to Ŝ(z) and is used for updating W(z) coefficient with error signal

e(n). As this algorithm works to minimize the square average of e(n), y(t)

becomes an out-of-phase signal from d(t). At the same time, u(n), which is

the output of W(z), goes through S(s) and becomes y(t). y(t) disappears when

it interacts with the disturbance signal d(t). Then, e(t), which is the error

Am

plitu

de

Diff

eren

ce [d

B]

Phase Difference (°)

15


16

signal, is generated (Kim, Park, Ryu, & Lee, 2015).

Figure 2.6 FXLMS Algorithm (Kim, Park, Ryu, & Lee, 2015)

The concept of ASD is opposite. ASD increases the level of specific

frequency bands whereas ANC decreases it. The FXLMS algorithm is used

for ASD as well, but in the case of ASD, the disturbance signal d(t) is the one

that should be kept, whereas it should be eliminated for ANC (Kim, Ryu,

Jang, Lee, Kim, Park, & Lee, 2015).

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17

2.3 ASD for Automobiles

Recently, an active control approach is widely used in the vehicle industry.

Particularly, the ASD and control techniques for vehicle interior sound are

mostly using ANC (Kim, Ryu, Jang, Lee, Kim, Park, & Lee, 2015). The

development of ASD algorithm has been led by Muller-BBM (German

company), Genesis (French company), and Neosonic (UK company).

Currently, the maturity of ASD software solution is high enough to be applied

to mass production.

Figure 2.7 Overview of ASD System in BMW F10 M5

(http://www.bimmerpost.com)

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18

Schirmacher (2002), in his study, stressed synergies that car infotainment

system will bring when parameter-dependent ASD is implemented. He also

introduced ASD application examples for modern car infotainment, which

has a digital amplifier, high-speed controller area network (CAN) bus, and

multiple microphones.

The ASD system can be easily implemented using the resources of modern

infotainment systems. Figure 2.8 shows a sketch of a typical ANC system for

interior engine noise reduction of a car consisting of an engine RPM sensor,

speakers, monitoring microphones, and an adaptive signal processing unit.

Figure 2.9 shows a sketch of a typical ASD system for interior engine noise

reduction of a car consisting of an ANC system plus an explicit target signal

definition process (Schirmacher, 2002).

Park, Jo, Hong, and Csakan (2015) developed two concepts of engine

sound using ASD. The intake sound concept emphasizes a specific powerful

frequency band across the harmonic orders to provide a feeling of power and

richness to the engine sound. They found that there are differences in

preferences for engine sounds depending on regions.

In North America, the intake sound concept with the frequency band and

half order emphasized was preferred, whereas in Korea, the exhaust sound

concept with the main order emphasized was preferred. In their study, a user-

defined engine sound system, which can be used for different regions of

which preferences of engine sounds are different, was developed.

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19

Figure 2.8 Sketch of a Typical ANC System for Interior Engine Noise Reduction of a Car Consisting of an Engine RPM Sensor, Speakers, Monitoring Microphones, and an Adaptive Signal Processing Unit

(Schirmacher, 2002)

Figure 2.9 Sketch of a Typical ASD System for Interior Engine Noise Reduction of a Car Consisting of an ANC System Plus Explicit Target

Signal Definition Process (Schirmacher, 2002)

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20

Drivers can select the type of engine sound and order setting and define the

sensitivity of accelerator pedal. Each setting can be stored in the AVN app

and can be recalled whenever the driver wants. The AVN app has four main

screens as depicted in Figure 2.10: engine sound selection, engine order

setting, accelerator pedal sensor (APS) setting, and sound box, which

provides a list of user-defined settings.

(a) Engine Sound Selection (b) Engine Order Setting

(c) APS Setting (d) Sound Box

Figure 2.10 AVN App Screen Layouts (Park et al., 2015)

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21

2.4 ASD for Electric/Hybrid Vehicles

Electric vehicles (EVs) and hybrid vehicles are becoming more popular

than before and it is not difficult to find these vehicles on the road. However,

because of lower sound level of engine, the accident rate is two times higher

than that of combustion engine cars (National Highway Traffic Safety

Administration, 2009). At a low speed under 50 km/h, this lower sound level

can be dangerous to pedestrians (Misdariis, Cera, Levallois, & Locqueteau,

2012). Regulations and sound design guides concerning the lower level

engine sounds of electric cars is still under study (Petiot et al., 2013).

Petiot et al. (2013) conducted a jury test with 40 participants, whose age

were 21 to 67, for 17 engine sounds. All participants had normal hearing

ability. The result in Table 2.2 shows that most of the participants prefer

adding sounds to EVs.

Table 2.2 Opinion on Adding Sounds to EVs (Petiot et al., 2013) Users

for

Users

against

Users no

opinion

Experts

for

Experts

against

57% 23% 20% 67% 33%

They also found that the most dominant answer from users was that an

electric car should sound like a regular car and resemble a combustion engine

sound as shown in Figure 2.11. This result is in line with previous studies

(Wogalter, Ornan, Lim, & Chipley, 2001; Nyeste & Wogalter, 2008).

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22

Fig

ure

2.1

1 W

hat

Sh

ould

an

Ele

ctri

c V

ehic

le S

oun

d L

ike?

(P

etio

t et

al.,

201

3)

22

CHAPTER 3. SONIC BRANDING PROCESS

23


3.1 Sonic Branding Process

In this chapter, a sonic branding process for an ASD system, by which

engine sound can be efficiently developed for a target brand identity and

image of car class is proposed. Sonic branding means creating or managing

brand value by using sound (Sung, Choi, Chung, & Kim, 2011). Engine

sound can be designed using acoustic parameters calculated from a regression

model of engine sound based on the target position, which is defined on the

two-dimensional positioning map for the brand.

The main framework of the process is designed by reviewing previous

studies on the AUI development process, considering that engine sound is

considered as an AUI in the automotive field currently. Previous studies on

AUI based on standardized techniques for developing and evaluating public

information symbols (ISO/DIS 7001:1979) were reviewed to develop a

process that secure high reliability of engine sound development. There are

five parts of the process, which consist of 13 stages as shown in Figure 3.1.

Part 1, Regression Model for Engine Sound, consists of 6 stages

including establishing the need for sound identity for a given referent brand,

adjectives for engine sound, acoustic parameters for engine sound, jury test,

factor analysis, and regression analysis. Two representative adjectives are

extracted during the factor analysis stage. Then regression equations, which

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24

are developed in Chapter 4 by statistical analysis for representative adjectives

and acoustic parameters, are applied into the regression analysis stage.

Part 2 is Brand and Synthesis Method, which consists of defining target

position and the synthesis method selection. When defining the target

position, the position of target engine sound on the two-dimensional

positioning map is defined based on brand identity and car class of target car.

Each axis of dimension for the positioning map is used for two representative

adjectives extracted from Part 1. Depending on the concept of target engine

sound, the synthesis method can be decided at the synthesis method selection

stage. Some synthesis methods are introduced and new synthesis methods are

proposed in Chapter 5.

Part 3, Engine Sound Design, consists of only one stage, i.e., prototype

engine sound design, at which the target engine sound is developed using the

acoustic parameters obtained from Part 1, and the synthesis methods selected

in Part 2. Collaboration with a sound design expert, who has insights for

sound design, is recommended at this stage. In Chapter 6, examples of engine

sound designs, with target positions on the positioning map and three

different synthesis methods for target cars, are introduced.

Finally, the engine sound designs are applied to the ASD systems in target

cars with 4 cylinders and 6 cylinders and new engine sounds are presented

through the ASD system.

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25

Part 4, Test and Validation, consists of the ASD system test, position

matching test, and operational test stages. The ASD system test stage is for

functionality test of the ASD system to ensure that it is working properly by

the parameters that have been set. During the position matching test stage, the

position of the new engine sound is compared with the target position, which

has been defined before the development. The operational test is the stage at

which a listening test is performed in driving condition. Examples of the test

and validation part are presented in Chapter 6.

Part 5, Verbal Description, consists of a standardized verbal description,

which is the last stage of the process. General information of engine sound,

including sound concept and synthesis method, is described for each engine

sound. The technical description, which is part of the verbal description and

technical information, including acoustic parameters, are provided in this

stage as presented in Chapter 6.

This approach will be helpful for improving the efficiency of sonic

branding for ASD systems. The reduction in development time can be

achieved in engine sound development by observing the directions in the

guideline. Moreover, a reliable engine sound development process can be

derived by following the guideline, because errors that can occur during the

engine sound development for ASD systems should be minimized. Figure 3.1

shows information about the sonic branding process and related chapters in

the study.

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26

Figure 3.1 Sonic Branding Process and Related Chapters

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27

3.2 AUI Design Process

Driving sound, electronic sound, and operation sound are three main

categories of car interior sounds. Traditionally, warning sound and alert

sound in the electronic sound category, which provides information and

feedback to driver, had been considered as AUIs in the past. However, as the

role of engine sound becomes more important than before as a means of

interaction between driver and car, engine sound in the driving sound

category is being considered as the AUI currently in the automotive industry

(Park, 2013; Jeon et al., 2015; Francois, 2017). Figure 3.2 shows the

categories of sounds in a car, as reported by Park (2013).

Figure 3.2 Categories of Sounds Presented in a Car Modified from Original Version (Park, 2013)

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28

Previous studies or international guidelines on the ASD development

process are few. Instead, there were some attempts to adapt ISO/DSI

7001:1979 guideline to AUI development in previous studies. ISO/DIS

7001:1979 is a standardized technique for developing and evaluating public

information symbols, which can be used for design and evaluation of product

sound. Easterby and Zwaga (1984) summarized and introduced the

standardized procedure for evaluating public information symbols in their

study as shown in Figure 3.3.

The procedure consists of six parts, namely production test, trial designs,

appropriateness ranking test, recognition test, matching process, and

operational test. In the production test, verbal or pictorial ideas about

description of images are generated by designers, based on the need for new

symbol for a given referent function. Then trial designs are generated from

situations and applications. Appropriateness of the image is ranked in the

appropriateness ranking test. In the recognition test, variants of the image of

the sounds are tested. At the final stage, which is the matching test, subjects

are given each referent one by one, and are asked to select, from the complete

set of symbols. Thus, this is a test for checking how well a set of symbols

works together. The symbols are then tested in operation condition in the

operational test part (Easterby & Zwaga, 1984).

In total, there are four visual tests of the four parts in the procedure,

namely appropriate ranking test, recognition test, matching test on symbol set,

and operational test. The procedure starts from verbal or pictorial ideas from

designers and trial designs are generated in the trial designs part. Following

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29

the procedure, symbols which are not appropriate, are not well recognized, or

do not match with the meanings, are removed from the list.

Figure 3.3 ISO Procedure for the Development of Public Information

Symbols (Easterby and Zwaga, 1984)

Then, there are studies adapting the ISO Procedure for the Development of

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30

Public Information Symbols to AUI design. Edworthy and Stanton (2007)

and Ulfvengren (2007) proposed an AUI design process in their studies.

Edworthy and Stanton (2007) adapted the design procedure for use in

designing and evaluating warning sounds and alert sounds as shown in Figure

3.4 based on the ISO procedure.

Figure 3.4 Diagrammatic Representation of Design Procedure

(Edworthy & Stanton, 2007)

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31

Figure 3.5 Cognitive Engineering Design Process for Natural Warning

Sounds and Auditory Alerting Systems (Ulfvengren, 2007)

Another attempt has been tried in several previous studies to develop a

user-centered design process for warning sound based on existing ISO/DIS

7001: 1979 guidelines. Ulfvengren (2007) suggested a cognitive engineering

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32

design process that includes the methods of associability and sound imagery

as shown Figure 3.5. In addition, there are some practical studies that

describe the general process for sound development in cars. Kim, Park, Hong,

Sellerbeck, Fiebig, Csakan, & Apelian (2013) introduced a sound quality goal

setting procedure based on user preference in their study as shown in Figure

3.6

Figure 3.6 Research Process for Target Sound Development

(Kim et al., 2013)

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33

3.3 Stages in Sonic Branding Process

In the sonic branding process, which is proposed in this chapter, there are

five parts, namely regression model for engine sounds, brand and synthesis

method, engine sound design, test and validation, and verbal description.

These five parts consist of 13 stages, namely establishing the need for sound

identity for a given referent brand, adjectives for engine sound, acoustic

parameters for engine sound, jury test, factor analysis, regression analysis,

defining target position, synthesis method selection, prototype engine sound,

ASD system test, position matching test, operation test, and standardized

verbal description.

Part 1, Regression Model for Engine Sound, consists of six stages,

namely establishing the need for sound identity for a given referent brand,

adjectives for engine sound, acoustic parameters for engine sound, jury test,

factor analysis, and regression analysis. Part 2 is Brand and Synthesis Method,

which consists of defining target position and synthesis method selection.

Part 3, Engine Sound Design, consists of only one stage, prototype engine

sound design. Part 4, Test and Validation, consists of the ASD system test,

position matching test, and operational test stages. Part 5, Verbal Description,

consists of the standardized verbal description, which is last stage of the

process.

Table 3.1 presents the list of stages composing each part. Each stage has an

input, which is used as the source for the stage, and an output, which is the

result of the stage. Sound identity defined is the output of establishing the

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34

need for sound identity for a given referent brand stage. The output of

adjectives for engine sound stage is affective adjectives pool. Acoustic

parameters pool is the output of acoustic parameters for engine sound stage.

Selected affective adjectives and representative adjectives are the input and

output of the factor analysis stage. The input and output of the regression

analysis stage are selected acoustic parameters and regression equations.

Brand identity/car class info and position on the positioning map are the input

and output of defining target position stage. Synthesis method selection stage

does not have any input or output. The input and output of prototype engine

sound design stage are range of parameter and engine sound for ASD. Order

measurement and position calculation are the inputs of ASD system test and

positioning matching test stages. There are no inputs or outputs for the

operational test and standardized verbal description stages.

The details of each part and stage in the process are described in Sections

3.3.1 to 3.3.5. A block diagram of the process is shown in Figure 3.7.

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35

Tab

le 3

.1 F

ive

Par

ts a

nd 1

3 St

ages

of

Son

ic B

ran

din

g P

roce

ss

35


36

36


37

Fig

ure

3.7

Blo

ck D

iagr

am o

f S

onic

Bra

nd

ing

Pro

cess

for

AS

D

37


38

Table 3.2 Comparison between Previous AUI Development Process and Sonic Branding Process Proposed in this Study

Source Process in this

Study Edworthy et. al.

/ Ulfvengren

Number of Stages

13

11

Number of Listening Test

2

(including Operation Test)

5

(including Operation Test)

Test Method

Listening Test (for analysis)

ASD System Test Position Matching Test

Operation Test

Listening Test

Modeling (Regression Equation)

YES

NO

Target Sound Definition

YES

(Positioning Map)

NO

Final Result

Sound Design for ASD

Sound Samples

Table 3.2 shows a comparison between the AUI development process

proposed by Edworthy and Stanton (2007) and Ulfvengren (2007) and the

sonic branding process in this study. The regression model for engine sound

is conducted for engine sound test in the sonic branding process proposed in

this study, whereas only the listening test is used in previous studies. An

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39

engine sound design, based on target sound definition, is available for the

process proposed in this study. In previous studies on AUI, a pool of sound

samples is prepared in the beginning of the process and then final sound sets,

which match the referents, are selected through five listening tests.

3.3.1 Part 1 : Regression Model for Engine Sound

Figure 3.8 Block Diagram for Part 1: Regression Model for Engine

Sound of Sonic Branding Process

Six stages, namely establishing the need for sound identity for a given

referent brand, adjectives for engine sound, acoustic parameters for engine

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40

sound, jury test, factor analysis, and regression analysis, comprise Part 1,

Regression Model for Engine Sound. In this part, a jury test is performed for

engine sound samples to rate the engine sound characters. For this, adjectives

related to engine sound characters were extracted from previous studies and

interviews with experts. Two representative adjectives are extracted during

the factor analysis stage.

Then regression equations are extracted by statistical analysis for

representative adjectives and acoustic parameters. The results are applied in

the regression analysis stage. Following the stages in this part, a regression

model for engine sound is built for two representative adjectives in Chapter 4.

They are regression equations for Refined and Powerful and two acoustic

parameters were used as variables for each equation.

(a) Establishing the Need for Sound Identity for a Given Referent

Brand

For alert sound design, the need for warning sound is investigated in

operational condition during establishing the need for warning for a given

referent function stage (Edworthy & Stanton, 2007). In the case of engine

sound, adjectives, which can be used to build up brand identity for a target

car, can be listed up in operational situations. In the block diagram,

establishing the need for sound identity for a given referent brand is first

stage of the process and the output of this stage can be a defined sound

identity.

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41

(b) Adjectives for Engine Sound / Acoustic Parameters for Engine

Sound

Previous studies by Dunne (2009) and Otto (1997) show how emotional

marketing statements can be used in subjective listening evaluations to

develop semantic target profiles. These target profiles can then be used to

generate target sounds for use during hardware development.

Abe, Clapper, McCarthy, Rubio, and Schabel (2004) conducted a sound

characterization jury test for four car groups. The adjectives that had been

used for sound characterization in the study can be examples of adjectives for

sound identity. Figure 3.9 shows an expert powertrain sound characterization

from the study by Abe et al. (2004).

Figure 3.9 Expert Powertrain Sound Characterization (Abe et al., 2004)

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42

Edworthy and Stanton (2007) describes existing and modified sounds as

the stage where the designer gathers existing warning sounds, and modifies

for alert sound design. Liberal ideas for potential sounds are generated in

generate trial sounds. In the case of engine sound, adjectives, which present

sound identity as shown in Figure 3.9, are gathered in the adjectives for

engine sound stage. In addition, acoustic parameters, which are required for

the regression model for engine sound, are gathered for the acoustic

parameters of engine sound stage. To gather adjectives and acoustic

parameters, a literature review and interview with experts can be used. The

outputs of these two stages are affective adjectives pool and acoustic

parameters pool.

(c) Jury Test

For alert sound design, at least five listening tests are conducted in the

process proposed by Edworthy and Stanton (2007) and Ulfvengren (2007).

The numbers of tests can be acceptable as the setup of listening test for alert

sound is not complicated. However in the case of engine sound, the setup for

listening test is much more complicated than the setup for alert sound. A

target car, which is equipped with an ASD system, is required and the test

should be done while driving at a test track. In this study, a regression model

is used for evaluation and development of the engine sound. The regression

model is a set of regression equations for representative adjectives, by which

the levels of adjectives are calculated with the acoustic parameters of engine

sound.

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43

For this, a jury test for engine recording samples is conducted during the

jury test stage. The input of this stage is selected affective adjectives. The

jury test is conducted for 38 engine sound recording samples by 42

participants to build a regression model for engine sound, as presented in

Chapter 4.

(d) Factor Analysis / Regression Analysis

For auditory sound development, the main object of the appropriateness

ranking test stage is establishing the most appropriate of the trial sounds for

each of the referents. Typically, this would result in two or three sounds. It is

suggested that only the top ranked and rated sound for each referent goes

forward to the next phase, because of the impact of greater numbers on the

confusion test to be carried out later (Edworthy & Stanton, 2007). Similarly,

two representative adjectives, from the affective adjectives pool, which

represent the engine sound characters, are selected in the factor analysis stage.

Factor analysis for a subjective jury test can be used to group adjectives that

present engine sound characters in the pool and obtain representative

adjectives from them. The inputs of this stage can be the selected affective

adjectives and the outputs can be representative adjectives. In addition, there

is a regression analysis stage. In this stage, the casual relationship between

representative adjectives and acoustic parameters are analyzed. Correlation

analysis and regression analysis can be used to build an engine sound design

model based on the result of statistical analysis. The inputs of this additional

stage are selected acoustic parameters and the outputs are regression

equations.

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3.3.2 Part 2 : Brand and Synthesis Method

Figure 3.10 Block Diagram for Part2 : Brand and Synthesis Method of Sonic Branding Process

Two stages, namely defining target position and synthesis method selection,

comprise Part 2, i.e., brand synthesis method. When the decision is made for

brand identity and car class for target car, the target position of engine sound

of target car is defined on the positioning map, which is developed as

discussed in Part 1. Then a synthesis method, which matches with the

concept of the engine sound, is selected for engine sound design. In Chapter

5, various synthesis methods, which can be applied to engine sound design,

are introduced. Recently, musical harmonic theory and formant filter have

been tested for engine sound design by car manufacturers and pipe organ

mixture, break back, and beat effect are proposed in this study.

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45

(a) Defining Target Position

Edworthy and Stanton (2007) introduced the design trial warning set as the

stage in which the final warning sets are generated. This set of prototype

sounds goes to the next stage for testing and development of alert sound. It

has been adapted as define target position stage for the sonic branding

process. In this stage, the position of target engine sound, which matches with

the brand and car class, is defined on the positioning map.

Then, proper values of acoustic parameters are proposed from the

regression model for engine sound, which has been built in Part 1. Brand

identity or car class info can be an input in this stage and position on the

positioning map is the output. Figure 3.14 shows an example of target

position based on the levels of adjectives defined at this stage.

(b) Synthesis Method Selection

After the target position is defined, a synthesis method, which matches

with the concept of target engine sound, can be selected at the synthesis

method selection stage. Musical harmonic theory is one of the common

synthesis methods that is tried by car manufacturers currently to provide

musical concepts to engine sound. Formant filters and biomimetic methods

have been tried recently to provide a strong character to engine sound. New

synthesis methods, i.e., pipe organ mixture and break back, which are playing

methods of pipe organs, are proposed as explained in Chapter 5. In addition,

beat effect, which is a result of differences in two frequencies, is introduced.

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46

3.3.3 Part 3 : Engine Sound Design

(Prototype Engine Sound Design)

Figure 3.11 Block Diagram for Part 3 : Engine Sound Design of Sonic Branding Process

In the study on AUI design process by Edworthy and Stanton (2007) and

Ulfvengren (2007), the position of the design prototype warning set is after

the learning/confusion and urgency mapping test stages. It is the stage, at

which designers can check if prototype sets are generated properly

considering previous trial cases (Edworthy & Stanton, 2007).

However, the position of prototype engine sound design stage for engine

sound design is before ASD system test and positioning matching test stages.

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47

In this stage, the engine sound is designed based on an acoustic parameter

range estimation, which is obtained from regression equations for the defined

target position. A synthesis method, which is selected in a previous stage, is

also considered for engine sound design in this stage. The resulting engine

sound design is applied to the ASD system, which is equipped in the target

car, in this stage. New engine sounds are recorded with RPM signals to be

used for the ASD system test stage as shown in Figure 3.12. This is the

reason that the position of this stage is before the ASD system test and

positioning matching test stages. The inputs of this stage can be range

parameters and the output can be engine sounds for ASD. In addition, the

recording sample of synthesized engine sound is used for order measurement,

and then the result of measurement is used as input to the next stage, ASD

system test., Engine sound design and tests were performed for 2 target cars

with 4 cylinders and 6 cylinder engines, as discussed in Chapter 6. Three

different engine sounds with different synthesis methods were developed for

each target car.

3.3.4 Part 4 : Test and Validation

Part 4, Test and Validation, consists of 3 stages: ASD system test, position

matching test, and operational test. In ASD system test, order levels of target

engine sound and synthesized engine sound are compared to check if ASD

systems in the target cars work properly. Positions of target engine sound and

synthesized engine sound on the positioning map are compared to check

appropriateness of new engine sound during the position matching test stage.

Then the operational test in driving condition is performed during the

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48

operational test stage.

Figure 3.12 Block Diagram for Part 4 : Test and Validation of Sonic Branding Process

(a) ASD System Test

The auditory warning set is tested for learnability and possibility of

confusion during the learning/confusion test stage. Urgency mapping test is

the stage in which the urgency of the warning used is matched to the urgency

of the situation for which it was designed as this is likely to improve its

efficiency (Momtahan & Tansley, 1989). These two stages are merged and

adapted to the ASD system test for engine sound design. In this stage,

functionality of the ASD system is tested with the main order level of engine

sound design. For this test, the main order level of synthesized engine sound,

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49

which has been recorded at the design prototype engine sound set stage, is

measured. Then the result of measurement is compared with the target level

defined in Part 2, to find the difference. The input of the stage ASD system

check is the order measurement.

Previous studies show the appropriateness of this approach. Lee, Kim, Ryu,

Kim, and Park (2016), compared the redefined target profile of artificial

sound to the original engine sound with the result of an enriched sound by the

active sound enrichment (ASE) algorithm, which is a similar concept to ASD.

The ASE control results show that the actively enriched sound of each engine

order against RPM tracks the target profiles precisely and quickly and

improves the discontinuity, the level ratios, and the sound pressure level of

each engine order as shown Figure 3.13 (Lee et al., 2016). In this study, the

loudness level, which is psychoacoustic, was proposed and designed

considering the human ear characteristics.

Figure 3.13 Target Profile and Before/After ASE (Lee et al., 2016)

49


50

(b) Position Matching Test

For the AUI design procedure of Edworthy and Stanton (2007), the

participant matches the warning to what he or she thinks is the most

appropriate referent at the recognition/matching test stage. In the case of

engine sound design, a comparison test for position of synthesized engine

sound and original engine sound on the positioning map is conducted at the

position matching test stage. Synthesized engine sounds are recorded during

the prototype engine sound design stage and acoustic parameters of

synthesized engine sounds are measured. Then, the levels of representative

affective adjectives are calculated from the regression equations, which were

built in Part 1 using correlation and regression analyses.

Figure 3.14 shows an example of target position, which is defined during

the defining target position stage, and position of synthesized engine sounds,

which is generated in Part 3, on the positioning map. The input of this stage is

position calculation. In case the positions of synthesized engine sounds are

out of the range of target position, the stage goes back to the prototype engine

sound design stage in Part 3, for a new engine sound design.

The positions of three target engine sounds are defined on the positioning

map with Refined and Powerful dimensions, as described in Chapter 6.

Positions of synthesized engine sounds are also presented on the positioning

map, based on acoustic parameter measurement. The result shows that the

positions of synthesized engine sounds are in the range of target position.

50


51

Figure 3.14 Target Positioning and Position of Prototype Engine Sound

(c) Operational Test

An operational test should be performed with the working environment as

closely as possible (Edworthy & Stanton, 2007). The operational test for

engine sound design should be conducted in a car with real driving condition.

The test car should be equipped with an ASD system and the final engine

sound design should be applied in advance. Moreover, the test should be

performed by experts who have experiences in engine sound development. At

least two experienced experts should participate to avoid any biased result for

the operational test. During the operation test in driving condition, it is

recommended to switch the driver’s role from one participant to another to

evaluate the engine sound not only as a passenger but also as a driver.

51


52

As the result of engine sound design has been validated during the

previous stages, no major modification is expected in the order tracking test

and positioning matching test stage. However, the result of test at this stage

can be used for minor modification of engine sound synthesis considering

real driving condition; particularly, the balance between the engine sound and

artificial engine sound from the speakers in a car generated by the ASD

system can be adjusted.

The level of sound from the ASD system can be easily controlled by the

ASD software tuning tool, which is usually installed on a personal computer

(PC). In Chapter 6, an example of operational test is presented. During the

test, the levels of sound from the ASD system for some target cars were

increased to adjust the balance between original engine sounds and

synthesized engine sounds

3.3.5 Part 5 : Verbal Description

(Standardized Verbal Description)

The purpose of generating standard verbal description, in the case of public

information systems, is to let people understand the concept of each symbol.

Table 3.3 shows the standardized verbal descriptions for warning sounds,

which describes the general information about each warning sound.

Additionally, Table 3.4 shows the technical descriptions of those warning

sounds including acoustic parameters that were used to generate warnings

(Edworthy & Stanton, 2007).

52


53

Figure 3.15 Block Diagram for Part 5 : Verbal Description of Sonic Branding Process

Table 3.3 Example of Standardized Verbal Description (Edworthy & Stanton, 2007)

For engine sound design, brand identity, sound characters, and information

about related affective adjectives are included in the standardized verbal

description. Target position on the map can be added to the standardized

verbal description to provide visual information. In the technical descriptions

for engine sound design, information about related acoustic parameters and

53


54

their values are included. The result of order level measurement can be added

to the technical description for engine sound design.

Table 3.4 Example of Technical Descriptions of Warnings Described in Table 3.3 (Edworthy & Stanton, 2007)

54


55

3.4 Discussions

In this study, the sonic branding process for ASD systems is proposed by

which sonic branding for engine sound can be conducted for target brand

identity and image of car class efficiently. Considering that the engine sound

is one of AUIs in a car, previous studies on AUI design process were adopted

to build the main structure of the process to maintain reliability.

In the process, five parts for engine sound evaluations and designs were

developed by affective engineering methods including rating of product

characteristic, interviews, and questionnaires. Pools of acoustic parameters

and affective adjectives were obtained by a literature review and interview

with experts. A jury test for rating of engine sound characters was conducted

with the adjectives. Moreover, the casual relationship between affective

adjectives and acoustic parameters are analyzed by correlation and regression

analyses. Then, the regression model for engine sound, which consists of a

set of regression equations for representative adjectives and acoustic

parameters, is derived. Positioning map and two-dimensional grid, which

have been used in previous studies, is proposed as a visual tool to evaluate

engine sound characters. This will be helpful for the efficient engine sound

design. Guides for the range of parameters were also described based on the

study on psychoacoustic and acoustic parameters in Chapter 4.

In the process, there are 13 stages, namely establishing the need for sound

identity for a given referent brand, adjectives for engine sound, acoustic

parameters for engine sound, jury test, factor analysis, regression analysis,

55


56

defining target position, synthesis method selection, prototype engine sound,

ASD system test, position matching test, operation test, and standardized

verbal description, which comprise five parts, namely regression model for

engine sound, brand and synthesis method, engine sound design, test and

validation, and verbal description.

By following the process proposed in this chapter, an efficient and reliable

sonic branding for engine sound will be achieved. The development time

could be reduced considerably and the possibility of success in the engine

sound synthesis is expected to increase.

56

CHAPTER 4. REGRESSION MODEL for ENGINE SOUND

CHAPTER 4. REGRESSION MODEL FOR ENGINE SOUND

4.1 Regression Model

In this chapter, a regression model for engine sound is proposed based on

the statistical analysis for affective adjectives and acoustic parameters.

Affective adjectives were selected from previous studies on engine sound

quality evaluation. Acoustic parameters are composed of psychoacoustic

parameters, which have been used in the past studies, and additional acoustic

parameters, which are proposed in this study. Figure 4.1 shows an example of

a regression model for engine sound.

Figure 4.1 Example of Regression Model for Engine Sound

First, a jury test was conducted to evaluate the level of nine affective

adjectives, which were selected from previous studies on engine sound

quality evaluation, for each engine sound recordings. Engine sounds of 38

57


cars, which are composed of compact, luxury, and sporty cars, were recorded

in WOT condition and evaluated by 42 participants.

Second, factor analysis was performed from the result of jury test to find

factors for the nine adjectives and group them. Then two representative

adjectives Refined and Powerful, which are used for dependent variables for

the regression equations, were obtained from the factor analysis.

Third, psychoacoustic parameters, which were chosen from literature

review and interview with experts, were measured by a measurement

software tool. Additional acoustic parameters, proposed in this study, were

measured as well. All measurements were done in the range of 2,000–5,000

RPM.

Fourth, correlation and regression analyses were conducted for two

representative adjectives and the measurement result of psychoacoustic

parameters and additional acoustic parameters. Then regression equations for

two affective adjectives were built based on the result of the statistical

analysis.

58


4.2 Subjective Measurement for Affect

Helander and Khalid (2006) introduced subjective measurement methods

for affect. Table 4.1 presents a list of subjective measurement methods in

their study. Subjective rating, interviews, and questionnaires in the list were

used to build the regression model for engine sound as described in this

chapter.

Various studies on engine sound analysis have been conducted. Most of the

previous studies on regression models for engine sounds were relationships

between affective adjectives and psychoacoustic parameters, and studies that

proposed regression models for engine sound based on acoustic parameters

are few. Static analysis including factor analysis, correlation analysis, and

regression analysis were used to build regression models for engine sound in

previous studies. Factor analysis and regression analysis were used by

Murata, Tanaka, Takada, and Ohsasa (1993) in their study on sound

evaluation to determine the factors including comfortable, powerful, and

booming, and to build a regression model based on these factors and acoustic

parameters. Takanami, Iwahara, Saito, and Sakata (1991) used factor analysis

and correlation to extract and study the factors including high grade feeling,

feeling of power, and metallic feeling. Factor analysis has been used for other

studies by Takao, Hashimoto, and Hatano (1993), Bisping (1997), Kubo,

Mellert, Weber, and Meschke (2004a), Buss, Schulte-Fortkamp, and Muckel

(2000), Kuwano, Fastl, Namba, Nakamura, and Uchida (2002), and Lee, Cho,

Seo, Lim, and Won (2012) to find meaningful factors from the analysis.

59


Table 4.1 Subjective Measurement Methods for Affect (Helander & Khalid, 2006)

In an article by researchers at Honda R&D, the regression equation for

sportiness is provided in the following general form below. The detailed info

60


about variables Coeff1, Coeff2, and Coeff3 are not shared, as they are

confidential information (Ishii et al., 2003). where OC is the level of order

content, T is a tonality-type metric, and ΔRPM describes the rate of change of

engine RPM.

Sportiness = Coeff1 × OC + Coeff2 × T + Coeff3 × ΔRPM

Semantic differential (SD) test is one of the common methods for engine

sound evaluation in previous studies. Rhiu et al. (2016) used a regression

analysis on his study to develop the SD scale from the analysis. SD test was

conducted by Krebber, Adams, Brandl, Chouard, Genuit, Hemple, Hofe, Irato,

Ponseele, Saint-Loubry, Schulte-Fortkamp, Sttek, and Weber (2000) in their

study on objective evaluation of interior car sound. Pared comparison was

used by Blommer, Amman and Otto (1997) to determine which combinations

of roughness and loudness levels contribute to the impression of performance.

Bisping (1995), Leite, Paul, and Gerges (2009) used a pared comparison test

as well. Recently, there are new attempts to use neural networks for engine

sound analysis. Lee, Park, Chai, and Jang (2005) used artificial neural

network theory for their study on objective evaluation of rumbling sound.

The result shows that neural network theory can be a useful method for

affective engine sound analysis, even though this analysis method does not

provide equations.

A previous study by Bisping (1997) for engine order level envelope

(EOLE) shows that pleasantness has relationship with the height EOLE in the

low frequency range and powerfulness is related to the change in low

61


frequency EOLE. A test with an online simulator in the study shows that

powerfulness can be increased only to a certain extent without affecting

pleasantness. If the trade-off range is exceeded, pleasantness is reduced

significantly as shown in Figure 4.2.

Figure 4.2 Magnitude Estimation of Pleasantness and Powerfulness

Depending on the Experimental EOLE increase (Bisping, 1997)

Figure 4.2 shows that pleasantness remains unaffected at the 3 and 6 dB

conditions, whereas powerful can be observed to increase already at the 3 dB

condition. At the 6 and 12 dB conditions, powerfulness continues to increase

whereas pleasantness is reduced considerably (Bisping, 1997). Kubo et al.

(2004a) found that in the test case of acceleration, the dimension powerful is

interpreted as sporty and the dimension pleasant includes luxurious. In

constant speed, the powerful and pleasant dimensions have merged, or cannot

be discriminated. Kubo et al. (2004a) proposed sounds of accelerated motion

for classifying engine sound as they are more suitable than those of constant

62


speed. In the test case of acceleration, the dimension powerful is interpreted

as sporty and the dimension pleasant includes luxurious. In constant speed,

the powerful and pleasant dimensions have merged together.

From a previous study by Kubo et al. (2004a), it is found that the

preferences on engine sounds by drivers are strongly influenced by the car

they own. Forty five participants who are luxury, classic, and sporty car

owners evaluated the engine sounds of luxury, classic, and sporty cars. Sound

characters of MB S600, Audi A8, VW Polo, VW Touran, Jaguar X type, VW

R32 were judged for the evaluation. The result showed that each car owner

group has different trends in its judgments for the three types of cars. For

example, classic car owners’ judgments show a high negative correlation

between classic and sporty dimensions for classic cars (Figure 4.3). In

general, car participants are most sensible to the sound character that is

typical of their own car.

Figure 4.3 Judgment for Classic Cars by Classic Car Owners

(Kubo et al., 2004a)

63


4.3 Jury Test

4.3.1 Engine Sound Samples

Engine sounds for 38 cars, from well-known global brands (A, B, C, D, E,

F, G, H, I, J, and K), which are composed of compact, luxury, and sporty cars,

were recorded for jury test. All driving sounds have been recorded with a

HEAD ACOUSTICS head set recorder in 3rd gear WOT condition. In this

study, accelerated engine sounds were used for the jury test to avoid merged

dimensions, considering the result of the previous study by Kubo et al.

(2004a). Table 4.2 shows the list of 38 cars, of which the engine sounds were

used for the jury test.

4.3.2 Participants

Forty two people participated in the jury test. They are composed of 30

males and 12 females who have normal hearing ability. Their ages were from

20 to 40 years. All participants have driving experiences of more than 5 years.

To avoid owners’ preferences on car sound, which were found in the study by

Kubo et al. (2004a), luxury car or sports car owners were excluded from the

participants. After the jury test, the results of 6 people from 42 participants

were excluded because they were outliers from the statistical analysis.

Participants, whose answers are out of 2σ (standard deviation) for more than

30 questions, are considered outliers.

64


Tab

le 4

.2 L

ist

of 3

8 C

ars

for

Jury

Tes

t

65


4.3.3 Questionnaire

There were two parts of the questionnaire for the jury test. The first part

was for gathering personal information. Age, preferred car brand, driving

experience, and owned car model are asked. In the second part, the

participants are asked to put a score for nine affective adjectives, which are

extracted from previous studies on engine sound, namely sporty, fast, quiet,

sharp, soft, rumbling, harmonic, comfortable, and stable for 38 engine sounds.

Likert 7-point scales were used for the subjective rating. Overall satisfaction

was scored during the questionnaire as well.

Then factor analysis was conducted to find factors for nine affective

adjectives and group them. The main two factors, Refined and Powerful were

obtained from factor analysis, and were used for correlation and regression

analyses in this chapter.

4.3.4 Representative Adjectives.

Factor analysis was conducted based on the result of jury test. Table 4.3

presents the result of factor analysis after a varimax rotation. The result

shows that nine adjectives are grouped into two factors, which is agreement

with the results of study by Bisping (1997), Penne (2004), Gabriella (2009),

and Bodden and Belschner (2014). Refine was decided as a representative

word for comfortable, stable, harmonic, soft, and quiet. Powerful was

selected for fast, sporty, sharp, and rumbling. Table 4.4 presents the internal

reliability for Refined and Powerful and the values of Cronbach’s alpha are

larger than 0.7 for both cases.

66


Table 4.3 Result of Factor Analysis

Table 4.4 Result of Internal Reliability for Refined and Powerful Factor Affective Adjectives Cronbach's alpha

Refined

Comfortable

0.893

Stable

Harmonic

Soft

Quiet

Powerful

Fast

0.78 Sporty

Sharp

Rumbling

Affective Adjective Principal Factor

1 2

Comfortable 0.878 -0.131 Stable 0.848 -0.027

Harmonic 0.784 0.167

Soft 0.780 -0.271

Quiet 0.713 -0.387

Fast 0.191 0.826

Sporty -0.049 0.825

Sharp -0.299 0.650

Rumbling -0.424 0.562

Group name Refined Powerful

67


4.3.5 Acoustic Parameters

Based on the previous studies on the topics related to sound evaluation and

interviews with sound experts, psychoacoustic parameters were chosen for

the parameter pool, which has been made from previous studies.

Psychoacoustic parameters, including AwSPL, Roughness, Sharpness,

Loudness, were selected for the pool.

Additional acoustic parameters, including Main_Order, Hard_Dynamic,

Soft_Dynamic, Soft_Bright, Harsh_Bright, Var_Int, and Var_Hlf, were

proposed in this study to build the regression model for engine sound, which

have higher R2 values. Details on additional parameters are provided in the

table below.

Table 4.5 Additional Acoustic Parameters

Name Order SQ Parameters Calculation

Main_Order 2(4 cyl.) or 3(6 cyl.) Order Loudness Sum

Hard_Dynamic 1, 1.5, 2, 2.5 Order Loudness Sum

Soft_Dynamic 3,3.5,…, 10, 10.5 Order Loudness Sum

Soft_Bright 11,11.5,…,17,17.5 Order Loudness Sum

Harsh_Bright 18,18.5,…,20 Order Loudness Sum

Var_Int 1, 2,…20 Order Loudness Variance

Var_Hlf 1.5, 2.5….18.5, 19.5 Order Loudness Variance

68


4.4 Descriptive Analysis

Table 4.6 Result of Descriptive Statistics for Psychoacoustic Parameters

/Acoustic Parameters and Affective Adjectives

Min

Max Average

Standard deviation

Skewness Kurtosis

Psychoacoustic

Parameters (unit)

AwSPL (dB) 21.80 102.80 80.94 20.78 -1.674 2.435

Sharpness (acum) .87 1.65 1.07 .19 1.859 3.431

Loudness (sone) 7.10 30.00 16.49 5.14 .585 .474

Tonality (tu) .05 .31 .148 .06 1.059 .855

Acoustic

Parameters (unit)

Hard_Dynamic

(sone) 8.61

36.90 16.09 7.54 1.665 2.073

Main_Order (sone) 3.13 33.20 9.68 6.42 2.213 5.318

Var_Hlf (sone) 3.71

133.91 23.85 34.03 2.358 4.834

Affective

Adjectives

Powerful 3.08 6.00 4.63 .68 -.002 -.501

Refined 3.18 5.25 4.23 .56 -.016 -.636

69


The results of the experiment show the descriptive statistics of each

psychoacoustic parameter, acoustic parameter, and affective adjective. In the

psychoacoustic parameters of Table 4.6, the minimum, maximum, and

average values of AwSPL were 21.80, 102.80, and 80.94 respectively with a

standard deviation of 20.78. The minimum, maximum, and average values of

Sharpness were 0.87, 1.65, and 1.07 respectively with a standard deviation

of .19. The minimum, maximum, and average values of Loudness were 7.10,

30.00, and 16.49 respectively with a standard deviation of 5.14. The

minimum, maximum, and average values of Tonality were .05, .31, and .148

with a standard deviation of .06.

For acoustic parameters, the minimum, maximum, and average values of

Hard_Dynamic were 8.61, 36.90, and 16.09 respectively with a standard

deviation of 7.54. The minimum, maximum, and average values of

Main_Order were 3.13, 33.20, and 9.68 respectively with a standard

deviation of 6.42. The minimum, maximum, and average values of Var_Hlf

were 3.71, 133.91, and 23.85 respectively with a standard deviation 34.03.

These 3 psychoacoustic parameters were chosen as variables for regression

analysis by the correlation analysis described in Section 4.6. In the case of

the affective variables, the value of the minimum, maximum, and average for

Powerful were 3.08, 6.00, and 4.63 respectively with a standard deviation

of .68. Additionally, the value of the minimum, maximum, and average for

Refined were 3.18, 5.25, and 4.23 respectively with a standard deviation

of .56. The value of standard deviation for Powerful and Refined were less

than 1.

70


4.5 Correlation Analysis

4.5.1 Correlation Analysis

Four engine sounds, of which RPM values are not recorded properly, were

excluded for correlation analysis. The correlation analysis was performed for

34 engine sounds, which has normal acoustic parameters from 38 recording

samples to find a correlation between psychoacoustic parameters in the pool

and the main affective adjectives, Refined and Powerful. From an interview

with experts in audio affective engineering, the four psycho parameters,

namely AwSPL, Roughness, Sharpness, and Loudness, were chosen for

correlation analysis with Refined and Powerful. Additional acoustic

parameters, which are given in Table 4.5 including Main_Order,

Hard_Dynamic, Soft_Dynamic, Soft_Bright, Harsh_Bright, Var_Int, Var_Hlf

were used for correlation analysis as well, to determine their relationship with

the main affective adjectives, Refined and Powerful.

4.5.2 Analysis Result

The result of correlation analysis for psychoacoustic parameters is given in

Table 4.7. Only Sharpness (-.685) shows a strong correlation with Refined,

whereas AwSPL (-.655), Loudness (.798), and Tonality (.654) have a strong

relationship with Powerful. There is a very strong correlation between

AwSPL and Roughness (-.973). Thus, Roughness was considered to be the

same parameter as AwSPL and was removed from the candidate for

regression.

71


Tab

le 4

.7 C

orre

lati

on A

nal

ysis

for

Psy

choa

cou

stic

Par

amet

ers

72


Tab

le 4

.8 C

orre

lati

on A

nal

ysis

for

Ad

dit

ion

al A

cou

stic

Par

amet

ers

73


The result of correlation analysis for additional acoustic parameters is

presented in Table 4.8. The correlations between additional acoustic

parameters and Refine or Powerful are not as strong as those of

psychoacoustic parameters. Harsh_Bright has highest value not only for

Powerful (.515) but also for Refined (-.363). Soft_Dynamic has a strong

correlation with Soft_Bright, Var_Int, and Var_Hlf. Soft_Bright has a strong

correlation with Harsh_Bright and Var_Int also has a strong correlation with

Var_Hlf. Therefore, Soft_Dynamic, Soft_Bright, Harsh_Bright, and Var_Int

were removed for the regression model in Section 4.6 during the regression

analysis.

The final parameter candidates for regression model, which were obtained

by correlation analysis, are seven, namely AwSPL, Loudness, Sharpness,

Tonality, Main_Order, Hard_Dynamic, Var_Hlf.

Table 4.9 Additional Acoustic Parameters for Regression Analysis

Name Order SQ Parameters Calculation

Main_Order 2(4 cyl.) or 3(6 cyl.) Order Loudness Sum

Hard_Dynamic 1, 1.5, 2, 2.5 Order Loudness Sum

Var_Hlf 1.5,…19.5 Order Loudness Variance

74


4.6 Regression Analysis

4.6.1 Regression Analysis

A regression analysis for Refined and Powerful was carried out for 34

engine sounds to build a regression model for engine sound. Seven

psychoacoustic/acoustic parameters including AwSPL, Loudness, Sharpness,

Tonality, Roughness, Hard_Dynamic, Var_Hlf, were used for the regression

analysis. A stepwise method was used for the statistical analysis software tool.

Table 4.10 presents the result of the regression analysis for Refined, whereas

Table 4.11 provides the result of regression analysis for Powerful.

4.6.2 Result of Regression Analysis

Table 4.10 presents the result of regression analysis for the Refined

dimension. Refined is described by AwSPL, Sharpness, Main_Order,

Hard_Dynamic, Var_Hlf, Tonality, and Loudness. The coefficient of

determination (R2) for Refined is .719. (Adjusted R2 = .643, F = 9.486. p

= .000).

Table 4.11 provides the result of regression analysis for the Powerful

dimension. For Powerful, the regression model was described by parameters

Loudness and Tonality. The coefficient of determination for Powerful is .679

(Adjusted R2 = .659, F = 32.860, p = .000).

75


Table 4.10 Result of Regression Analysis for Refined Dimension and Psychoacoustic/Acoustic Parameters

Unstandardized Coefficient

Standardized Coefficient t p

B Std. Err beta

Constant 15.848 2.513 6.306 .000

AwSPL -.055 .015 -2.063 -3.737 .001

Sharpness -5.206 1.056 -1.806 -4.930 .000

Main_Order .116 .035 1.334 3.297 .003

Hard_Dynamic -.056 .029 -.762 -1.939 .063

Var_Hlf -.012 .005 -.738 -2.199 .037

Tonality -4.351 2.049 -.497 -2.124 .043

Loudness -.053 .020 -.488 -2.712 .012

Table 4.11 Result of Regression Analysis for Powerful Dimension and Psychoacoustic/Acoustic Parameters

Unstandardized Coefficient

Standardized Coefficient

t p

B Std. Err beta

Constant 2.821 .235 12.008 .000

Loudness .085 .017 .637 4.931 .000

Tonality 2.802 1.386 .261 2.021 .052

76


4.6.3 Regression Equation for Powerful and Refined

The regression equation of Refined is statistically significant at the .1 level,

and significant parameters were AwSPL, Sharpness, Main_Loud, Var_Loud,

Tonality, and Loudness. AwSPL and Sharpness have the largest contribution

to the Refined dimension. Figure 4.4 shows the regression equation for

Refined with beta values.

Figure 4.4 Regression Equation for Refined

Another simple regression equation has been made as shown in figure 4.5,

which is statistically significant at .05. The value of R2 is lower than the

original model (R2 = .554, Adjusted R2 = .525, F = 19.257, p = .000 ), but the

numbers of independent variables are two ,which can be easily applied to a

real engine sound development.

Figure 4.5 Additional Regression Equation for Refined

77


From the results, the regression equation for Powerful was statistically

significant at the .1 level and Loudness and Tonality have the largest

contribution to Powerful. Figure 4.6 shows the regression equation for

Powerful with beta values, based on the result of regression analysis.

Figure 4.6 Regression Equation for Powerful

4.6.4 3 Groups for Engine Sound Characters

Characters of engine sounds, which had been used for jury tests, were

grouped by three concepts as presented in Table 4.12. Engine sounds in

Group 1 have high levels for Refined and Powerful. Engine sounds in Group

2 have high Refined and low Powerful levels. Group 3 is for engine sounds

that have low Refined and high Powerful levels.

Table 4.12 3 Groups for Engine Sound Characters Refined Powerful

Min Max Min Max

Group 1 4.5 6 4.5 6 Group 2 4.5 6 3 4.5 Group 3 3 4.5 4.5 6

78


Figure 4.7 shows the positions of each engine sound groups with different

sound characters based on Table 4.12.

Powerful

Figure 4.7 Positions for 3 Engine Sound Groups

The range of acoustic parameters for each group are described in Figure

4.8 based on the acoustic parameter engine sounds that were used for the jury

test. The order of groups on the graph is arranged as Group 2, Group 3, and

Group 1 to show the trend that occurs in this order, instead of Group 1, Group

2, and Group 3. This information will be helpful to reduce the time for

finding proper values of acoustic parameters from the regression model of

engine sounds for given Refined and Powerful levels.

Ref

ined

79


(a)Sharpness (acum) (b) Tonality (tu)

(c) Main_Order (sone) (d) Hard_Dynamic (sone)

80


(e) Loudness (sone) (f) AwSPL (dB)

(g) Var_Hlf (sone)

Figure 4.8 Range of Acoustic Parameters by Group

81


4.7 Discussions

The regression model for engine sound, which is Part 1 of the sonic

branding process was proposed based on the statistical analysis. Affective

adjectives were selected from previous studies on engine sound quality

evaluation. Acoustic parameters, which are composed of psychoacoustic

parameters, which have been used in the past studies, and additional acoustic

parameters, which are proposed in this study, were used for statistical

analysis.

First, the levels of nine affective adjectives, which were selected from

previous studies on engine sound quality evaluation, were judged by 42

participants with normal hearing in a jury test for engine sound of 38 cars.

Second, two representative adjectives, Refined and Powerful, were obtained

from the factor analysis, which were performed from the result of jury test to

group the nine adjectives. Third, the values of acoustic parameters of 38

engine sounds were measured by a measurement software tool for the range

of 2,000–5,000 RPM, to be used for correlation and regression analyses.

Fourth, correlation and regression analysis were conducted for two

representative adjectives and the measurement results of the acoustic

parameters for 34 engine sounds to determine which acoustic parameters

have normal values.

Then the regression model for engine sound was built based on the result

of the statistical analysis.

82


AwSPL, Sharpness, Main Oder, Hard_Dynamic, Var_Hlf, Tonality, and

Loudness were used for the regression equation of Refined based on

statistical analysis. Another simple model, made with two variables including

Sharpness and AwSPL was proposed for application purpose. Loudness and

Tonality were used for the regression model for engine sound of Powerful.

In addition, characters of engine sounds were grouped into three concepts,

which have different levels of Refined and Powerful with proposed values for

acoustic parameters. This approach will be helpful to define the target

position on the positioning map, which has specific sound characters

intended and to reduce the time of finding proper values for design variables.

The R2 value of the regression model for engine sound for Refined is

higher (.719) with additional acoustic parameters, which are proposed in this

study, compared to the model with only psychoacoustic parameters (.554).

However, it is difficult to use additional acoustic parameters for a real ASD

development. More studies on acoustic parameters, which increase R2 value

and provide a simpler regression model for engine sound, are required in

future research.

The regression model for engine sound described in this chapter is applied

to the sonic branding process described in Chapter 3. In particular, during the

design of prototype engine sound and positioning matching test stages, the

model plays an important role.

83

CHAPTER 5. AFFECTIVE SYNTHESIS METHOD

CHAPTER 5. AFFECTIVE SYNTHESIS METHODS

5.1 Synthesis Methods

In this chapter, affective synthesis methods are introduced. The methods in

this chapter are applied to Part 2, Brand and Synthesis Method. Recently,

various engine sound synthesis methods are studied by car manufacturers to

improve affective quality of cars with engine sounds. There were some

attempts to adapt music composition theories to engine sound development.

The biomimetic method, for example, using formant filter, which imitates the

mouth structure, is studied previously. In addition, engine sound level

linearization, depending on RPM increase, is one of the topics in which car

makers are interested in.

New technologies, which have been researched and the proposed methods

that can be applied to ASD systems, are described in this chapter. Acoustic

characteristics of a pipe organ, whose structures are similar to those of intake

and exhaust systems, can be applied to engine sound synthesis. For example,

mixture break back methods can be adapted to lower orders in higher RPM to

provide more dynamic perception. Moreover, beat effect, which occurs in

two pipes with small differences in length, can be used to reproduce

vibrations in low frequencies generated by the exhaust system

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5.2 Examples of Synthesis Methods

There were attempts to apply musical harmonic theory to engine sound

analysis and synthesis in previous studies. Nobert, Alt, and Jochum (2003)

compared musical harmonic theory with engine sound. They proposed that

new insights regarding noise characteristics that are objectively melodious in

musical terms can be gained and transferred to the vehicle. Figure 5.1 shows

a comparison between engine orders and intervals in music.

Figure 5.1 Comparison between Engine Orders and Musical Intervals (Nobert et al., 2003)

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Yun, Park, Won, and Im (2012) analyzed a 6-cylinder engine order using

musical harmonic theory. Order frequencies were measured with RPM

signals and they were matched with musical intervals. Table 5.1 indicates that

the engine structure is very similar to musical harmonics and musical

harmony can be applied to engine sound by adjusting the level of specific

engine orders.

Table 5.1 Musical Harmonic Theory Analysis for 6-Cylinder Engine at 5880 RPM (Yun et al., 2012)

Order Frequency

(Hz) Pitch Order Frequency (Hz) Pitch

1 98 G2 6 588 D5

1.5 156 D3 6.5 635 D5#>E5

2 196 G3 7 686 E5>F5

2.5 245 B3 7.5 732 F5#

3 294 D4 8 784 G5

3.5 343 E4<F4 8.5 820 G5#

4 392 G4 9 882 A5

4.5 441 A4 9.5 928 A5#

5 491 B4 10 965 B5

5.5 542 C5<C5# 10.5 1039 C6#

There were attempts to apply the formant filter to engine sound design.

Formant analysis is a commonly used method to analyze acoustical

parameters of human voice for phonology and voice recognition. Chang and

Park (2016) proposed a formant filter of a tiger for a sporty engine sound

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design. Figure 5.2 shows the process of a target sound synthesis based on a

formant filter. Figure 5.3 shows an FFT analysis of a tiger’s cry for

estimating a formant filter (Chang & Park, 2016).

Figure 5.2 Process of Target Sound Synthesis Based on Formant Filter

(Chang & Park, 2016)

Figure 5.3 FFT Analysis of a Tiger’s Cry for Estimating a Formant Filter (Chang & Park, 2016)

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Beat is a modulation effect when there are two different sound sources at

the same time with small differences in frequency. Figure 5.4 shows a strong

variation of the temporal envelope (Zwicker & Fastle, 1999). This effect is

tested as described in Section 5.4.4 as a new synthesis method.

Figure 5.4 Time Functions and Associated Frequency Spectra of Stimuli Commonly used in Psychoacoustics (Zwicker & Fastle, 1999)

Engine sound level linearization, depending on RPM, was studied by Kim,

Chang, Park, Lee, and Lee (2017). In their study, an equation, by which the

amplitude of engine order can be calculated, was derived as given below.

(1)

For the value of k, 0.5 to 4 was proposed. In case k = 4, the amplitude

changes drastically depending on RPM increase. Figure 4.5 shows an

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example FFT analysis of a synthesized engine sound with different k values

(Kim et al., 2017).

Figure 5.5 FFT Analysis of Synthesized Engine Sound with Different k Values (Kim et al., 2017)

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5.3 Synthesis Methods for Engine Sound Design

5.3.1 Musical Harmonic Theory

Maserati is known to collaborate with music composers to design engine

sounds and have its own music sheet for engine sound. Figure 5.6 in part of a

well-known music sheet of Maserati engine sound.

Figure 5.6 Part of Music Score of Modified Version of Aria Nessun Dorma from Opera Turandot, which is Used for Maserati Engine

Sound Design (Ha, 2011)

In this study, the main order, which is the most dominant signal in engine

orders has been set as C1. It means that the 2nd order is C1 for a 4-cylinder

engine and the 3rd order is C1 for a 6-cylinder engine. Interval Prime, Major

3rd, Perfect 5th, and Octave, which are intervals for tonic chord, were mainly

used for engine sound synthesis. Major 2nd, Minor 3rd, or Perfect 4th, which

cause dissonance in tonic chord, were used in the case where rumble sound is

required for the engine sound design. Table 5.2 presents the frequency rate

for each interval.

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Table 5.2 Frequency Rate for Intervals

Interval Frequency Rate

Prime 1:1 1.0000

Minor 2nd 24:25 1.0417

Major 2nd 8:9 1.1250

Minor 3rd 5:6 1.2000

Major 3rd 4:5 1.2500

Major 4th 3:4 1.3333

Tritone 32:45 1.4063

Major 5th 2:3 1.5000

Minor 6th 5:8 1.6000

Major 6th 3:5 1.6667

Minor 7th 5:9 1.8000

Major 7th 8:15 1.8750

Octave 1:2 2.0000

5.3.2 Formant Filter

From a tiger’s two roaring recording samples, namely for Vigilant

condition and for Onslaught condition, two formant filter estimations were

conducted. Vigilant recording sample was used for Formant Filter 1 and

Onslaught recording sample was used for Formant Filter 2. Figure 5.7 shows

the FFT analysis for formant frequency estimation for a tiger’s roaring sound

sample.

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(a) FFT analysis for tiger’s roaring sound sample #1(Vigilant)

(b) FFT analysis for tiger’s roaring sound sample #2 (Onslaught)

Figure 5.7 FFT Analysis of Formant Frequency Estimation for Tiger’s Roaring Sound Sample

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5.4 Proposals for New Synthesis Methods

5.4.1 Pipe Organ

The pipe organ, which is called the king of instruments, is composed of

many pipes to generate various sounds, as shown in Figure 5.8.

Figure 5.8 Pipes of Pipe Organ

There are two types of pipes in a pipe organ. One is a closed pipe with a reed

and has a structure similar to a clarinet, and the other is an open pipe without

a reed and has structure similar to a flute. (Dickens, France, Smith, & Wolfe,

2007). Each has different harmonic structures as shown in Figure 5.9.

93


Figure 5.9 Harmonic Structure of Clarinet and Flute (Dickens, France, Smith, & Wolfe, 2007)

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5.4.2 Synthesis Method using Concept of Organ Mixture

Mixture is one of the famous pipe organ playing methods using multiple

pipes as the same time. The Roman numbers IV, V, and VI, following the

name of a mixture mean the numbers of the pipes that were used for mixture

playing.

The original purpose of mixture was to increase the sound level of organs.

However, currently, this method is more widely used for adjusting tone color

for pipe organs. Figure 5.10 shows an FFT analysis of a mixture playing in

Richard Strauss’ Zaratustra Intro. Harmonics from pipes can be found in the

spectrum, whose structure is similar to an engine order’s one.

Figure 5.10 FFT Analysis of Pipe Organ Mixture

(Richard Strauss – Zaratustra Intro)

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Figure 5.11 Music Note for Organ Mixture

(Richard Strauss – Zaratustra Intro)

Figure 5.11 shows the note for organ mixture in Richard Strauss’

Zaratustra Intro, which was used for the FFT analysis in Figure 5.10. Table

5.3 is a matching table for mixture harmonics and engine orders. The table

shows that a mixture harmonics structure is very similar to an engine order

structure. Therefore, musical harmonic theory can be applied to engine sound

design.

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Table 5.3 Pipe Organ Harmonics and Engine Orders Pipe Organ Engine Order

Number Pitch Frequency(Hz) Order Frequency(Hz) FFT(dB)

27 C8 12,544.00 384.00 12,560.00 32.91 26 C8 10,548.05 320.00 10,570.00 32.78 25 C8 8,372.00 256.00 8,376.42 38.33 24 G7 6,272.00 192.00 6,287.70 44.19 23 E7 5,274.03 160.00 5,286.40 48.61 22 C7 4,186.00 128.00 4,198.97 54.42 21 G6 3,136.00 96.00 3,143.85 60.83 20 E6 2,637.01 80.00 2,637.82 57.99 19 C6 2,093.00 64.00 2,099.49 65.20 18 G5 1,568.00 48.00 1,571.92 67.60 17 E5 1,318.51 40.00 1,324.29 66.68 16 C5 1,046.50 32.00 1,044.36 65.52 15 G4 784.00 24.00 786.00 64.81 14 E4 659.25 20.00 656.80 68.73 13 C4 523.25 16.00 527.60 67.08 12 G3 392.00 12.00 398.40 69.36 11 E3 329.63 10.00 333.80 72.12 10 C3 261.63 8.00 258.40 65.29 9 G2 196.00 6.00 193.80 76.38 8 E2 164.81 5.00 161.50 68.87 7 C2 130.81 4.00 129.20 73.62 6 G1 98.00 3.00 96.90 79.33 5 E1 82.41 2.50 4 C1 65.41 2.00 64.60 79.23 3 G0 49.00 1.50 2 E0 41.20 1.25 1 C0 32.70 1.00 32.30 87.50

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5.4.3 Mixture Break Back

Figure 5.12 Example of Mixture Break Back Based on the Interval

Perfect 4th and Perfect 5th

(https://en.wikipedia.org/wiki/Mixture_(music))

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Mixture break back is one of the pipe organ playing techniques to add

more stops, a group of pipes, for the notes in lower pitches. The main purpose

of this playing technique is to adjust the balance between notes in high

pitches and in low pitches. This method can be adapted to engine sound

synthesis to adjust tonal balance in higher RPM status.

Figure 5.13 shows the concept of an organ break back, which can be

applied to engine sound synthesis. C1, G1, C2, and E2 are notes that

comprise a harmonic sound. Then C0, which is one octave below C1, is

added. By adding C0, the balance between high pitches and low pitches can

be adjusted.

(Harmonic sound composed by C1, G1, C2, and E2)

(Break back)

Figure 5.13 Concept of Organ Break Back

+ + +

G C G C

C1 G1 C2 E2

C0

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5.4.4 Beat Effect

Figure 5.14 Beat Effect (https://en.wikipedia.org/wiki/Beat_(acoustics))

Beat is a modulation effect when there are two different sound sources with

small differences in frequency. This symptom also happens when there are

two pipes, whose lengths are slightly different.

This effect can be used for dynamic sounds on low frequency bands below

100 Hz for strong variation of the temporal envelope result in generating

modulations. Table 5.4 presents an example of engine sound design with beat

effect in a low frequency band. Figure 5.15 shows a temporal envelope

comparison between the original engine sound and synthesized engine sound

using beat affect. Much stronger variation of the temporal envelope is found

on Figure 5.15 (b) compared to Figure 5.15 (a).

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Table 5.4 Example of Engine Sound Design with Beat Effect in Low Frequency Band

Order/RPM 1000 2000 3000 4000 5000 6000

2 33.33(Hz) 66.67(Hz) 100.00(Hz) 133.33(Hz) 200.00(Hz) 200.00(Hz)

2.1 35.00(Hz) 70.00(Hz) 105.00(Hz)

2.5 41.67(Hz) 83.33(Hz) 125.00(Hz) 166.67(Hz) 208.33(Hz) 250.00(Hz)

(a) Original Engine Sound

(b) Synthesized Engine Sound Using Beat Affect

Figure 5.15 Temporal Envelope for Recording Samples of (a) Original Engine Sound and (b) Synthesized Engine Sound using Beat

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Figure 5.16 is an FFT analysis of engine sound with engine sound design

in Table 5.7. The result of beat effect can be found in the range in (b),

approximately 2nd order.

(a) Original Engine Sound (b) Synthesized Engine Sound

Figure 5.16 FFT Analysis of Engine Sound with Engine Sound Design in Table 5.4

The beat effect method, which generates strong modulation, can be

appropriate for sporty cars, but is not applied to the engine sound design and

test discussed in Chapter 6.

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5.5 Discussions

Affective synthesis methods, which are studied recently by car makers to

improve affective quality of cars with engine sounds, were introduced in this

chapter including musical harmonic theory, formant filter, and engine sound

level linearization depending on RPM. Musical interval for two notes can be

applied to engine orders as they have similar structures to musical harmonics.

Formant filter with biomimetic method is a method that imitates mouth

structure to provide the engine sound a signature of savage beast.

New engine sound synthesis methods, based on acoustic characteristics of a

pipe organ, including the mixture break back method and beat effect were

proposed. Mixture break back can be adapted to lower orders in higher RPM

to provide a more dynamic perception. Beat effect, which occurs in two pipes

with small differences in length, can be used to reproduce a strong

modulation in a low frequency band.

These new method, which are introduced and proposed in this study, will

be helpful to develop new affective engine sounds with a unique signature or

concept, which can appeal to customers.

103

CHAPTER 6. ENGINE SOUND DESIGN AND TEST


6.1 Engine Sound Design and Test

Engine sound design, which is Part 3 of the sonic brand process, is

conducted as discussed in this chapter following the directions in each stage

for the process. The target position for new engine sounds in two target

vehicles were defined on the Refined and Powerful positioning map, and

acoustic parameters for these positions were proposed through the regression

model for engine sound. Affective synthesis methods including musical

harmonic theory, formant filter, and mixture break back of pipe organ were

used for engine sound design in cooperation with a sound design expert.

The test and validation, which is Part 4 of the sonic brand process was

performed to show the feasibility of the process. An ASD system test was

conducted to check the functionality of the ASD system in the target car. A

position matching test was performed to compare the target position and

synthesized engine sound on the positioning map. An operational test in

driving condition was also conducted to check the balance between the

original engine sound and synthesized sound from the ASD system.

Finally, verbal description, which is Part 5 of the process, was performed

to provide information on new engine sounds.

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6.2 Target Vehicle and Engine Sound Samples

Two sporty class cars from Brand D, with 4-cylinder and 6-cylinder

engines have been selected as target vehicles for the ASD application test.

These cars are prototype test cars, equipped with ASD systems in external

amplifier for mass production test. The ASD system, equipped in the target

vehicles can be updated with different ASD tuning data, which are made on a

PC connected to the external amplifier through a signal cable. Through the

signal cable, the ASD system is controlled by tuning the PC in real time.

The original engine sounds the 4-cylinder and 6-cylinder vehicles were

recorded for ASD development at driver’s position in each target vehicle. The

duration of recording samples were approximately 15 s and the sound

pressure level (SPL) of two recording samples were 83 dBA. All engine

sounds have been recorded with a HEAD ACOUTICS head set recorder at

driver’s position in 3rd gear WOT condition. RPM information was recorded

with engine sound through a CAN signal cable.

Figure 6.1 shows that wave forms of recording samples and RPM data

recorded through CAN signals for 4-cylinder engine sound and 6-cylinder

engine sound.

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(a) Recording Sample of 4-Cylinder Engine

(b) Recording Sample of 6-Cylinder Engine

Figure 6.1 Wave Form of Recoding Samples for Target Cars with 4-Cylinder and 6-Cylinder Engine

106


6.3 Defining Target Engine Sound

6.3.1 Target Positioning for Engine Sound Character

Following the directions of the prototype engine sound design stage, the

positions for new engine sound were proposed on the positioning map. For

this, the range of Refined and Powerful values were defined to develop new

engine sounds based on the original 4-cylinder engine and 6-cylinder engine

sounds of target vehicles.

Table 6.1 Ranges of Refined and Powerful Levels for Target Positions

Refined Powerful

Min Max Min Max

4-cylinder 4.6 5.2 5.1 5.7

6-cylinder 4.5 5.1 5.6 6.2

Then, target positions for new engine sounds were defined on the

positioning map based on the values proposed in Table 6.1. Figure 6.2 shows

the target positions, which are defined for the 4-cylinder and 6-cylinder

engines.

6.3.2 Acoustic Parameters for Target Position

At first, the acoustic parameters of two original 4/6-Cylinders engine sounds

were measured to find the position of original sound on the positioning map.

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(a) Target Position for 4-Cylinder Engine Sound

(b) Target Position for 6-Cylinder Engine Sound

Figure 6.2 Position Proposed for Target Engine Sound

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Second, new positions were proposed to move the original position to the

intended position for target engine sounds. Then, the range of acoustic

parameters, which result in target positions, were obtained from the

regression model for engine sound built as described in Chapter 4, for each

engine sound as given in the equations below. Simple regression model was

used for Powerful, as shown in figure 4.5.

Refined = 10.863 – 4.406 × Sharpness – 0.024 × AwSPL (1)

Powerful = 2.821 + 0.085 × Loudness + 2.802 × Tonality (2)

The equation for Refined shows that its level is decreased when Sharpness

and AwSPL are increased. Sharpness has the most negative relationship with

Refined. For Powerful, it is needed to increase the levels of Loudness and

Tonality for a higher Refined level. Tonality has the strongest relationship

with Powerful.

Table 6.2 indicates the acoustic parameters measured in the target cars at

driver’s position with WOT condition. Each value of acoustic parameter is

used as a reference and it has been adjusted from the original value to the

new value, which results in the target position.

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Table 6.2 Acoustic Parameters for Original Engine Sound

Index (unit) 4 cylinders 6 cylinders

Sharpness (acum) 0.95 1.04

AwSPL (dB) 85.5 77.8

Loudness (sone) 22 24.9

Tonality (tu) 0.0696 0.0764

Table 6.3 shows the range of acoustic parameters for the 4-cylinder and 6-

cylinder target cars. Each range of acoustic parameters is set considering

values of engine sound Group 1, of which the position is close to target

positions (high Refined level and high Powerful level).

In the case of 4-cylinder engine, 0.9–0.95 acum is the range of Sharpness,

80–85.5 dB is the range for AwSPL, 22–30 sone is the range of Loudness,

and 0.07–0.23 tu is the range of Tonality. For the 6-cylinder engine, 0.9–0.95

acum is the range of Sharpness, 65–77.8 dB is the range of AwSPL, and

24.9–35 sone is the range of Loudness, and 0.07–0.23 tu is the range of

Tonality.

Table 6.3 Range of Acoustic Parameters for Target Positions

Index (unit) 4 cylinder 6 cylinder

Min Max Min Max

Sharpness (acum) 0.9 0.95 0.9 0.95

AwSPL (dB) 80 85.5 65 77.8

Loudness (sone) 22 30 24.9 35

Tonality (tu) 0.07 0.23 0.076 0.23

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6.4 Engine Sound Synthesis

6.4.1 Synthesis Methods for Target Engine Sounds

Based on the order level obtained from the simulation tool, new engine

sound synthesis methods, which have been introduced in Chapter 5, were

applied for affective engine sound design.

Three concept engine sounds, for which different synthesis methods were

used, were synthesized for each of the 4-cylinder and 6-cylinder engine

sounds. Table 6.4 presents the engine sound synthesis methods for the target

engine sounds.

Table 6.4 Engine Sound Synthesis Methods for Target Engine Sounds

Name Engine Sound #1 Engine Sound #2 Engine Sound #3

Synthesis Method

Mixture Break Back

Musical Harmonic Theory Formant Filter

Cylinder

4 Prime, Major 3rd, Perfect 5th, Octave

Prime, Major 3rd, Perfect 5th, Octave


Perfect 4th

6 Prime, Major 3rd,

Perfect 5th, Octave Minor 3rd


Perfect 4th

Prime, Major 3rd, Perfect 5th, Octave Major 2nd, Perfect

4th

Concept Pipe Organ

with Break Back Musical Harmony Tiger Roaring

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6.4.2 Application of Musical Harmonic Theory

Orders for engine sound synthesis were selected based on the musical

harmonic theory. Table 6.5 and 6.6 present the musical intervals, which are

used for engine sound synthesis for the 4-cylinder and 6-cylinder engines.

In the table, CON means Consonance and DIS means Dissonance in the

tonic chord. For the 4-cylinder engine sound, Prime, Major 3rd , Perfect 5th,

and Octave are mainly used for the musical intervals. In the case of 6

cylinders, Prime, Major 3rd, Perfect 5th, and Octave are the main intervals for

engine sound synthesis.

Table 6.5 Intervals for 4-Cylinder Engine Sound Synthesis

(a) Intervals for 4-Cylinder Sample #1 Note Order Interval CON/DIS

C0 1 Prime CON

C1 2 Octave CON

E1 2.5 Major 3rd CON

G1 3 Perfect 5th CON

C2 4 Octave CON

E2 5 Major 3rd CON


C3 8 Octave CON

E3 10 Major 3rd CON


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(b) Intervals for 4-Cylinder Sample #2 Note Order Intervals CON/DIS

C1 2 Prime CON



C2 4 Octave CON

E2 5 Major 3rd CON


C3 8 Octave CON

(c) Intervals for 4-Cylinder Sample #3 Note Order Intervals CON/DIS

C1 2 Prime CON



F 3.5 Perfect 4th DIS

C2 4 Octave CON

E2 5 Major 3rd CON

G2 5.5 Perfect 5th CON

C3 6 Octave CON

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Table 6.6 Intervals for 6-Cylinder Engine Sound Synthesis

(a) Intervals for 6-Cylinder Sample #1 Note Order Intervals CON/DIS

A0 1.5 Minor 3rd DIS

C1 3 Prime CON


C2 6 Octave CON

E2 7 Major 3rd CON


C3 12 Octave CON

E3 15 Major 3rd CON

(b) Intervals for 6-Cylinder Sample #2 Note Order Intervals CON/DIS

C1 3 Prime CON

F1 4 Perfect 4th DIS


C2 6 Octave CON



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(c) Intervals for 6-Cylinder Sample #3 Note Order Intervals CON/DIS

C1 3 Prime CON

D1 3.5 Major 2nd DIS

F 4 Perfect 4th DIS


C2 6 Octave CON


6.4.3 Application of Organ Break Back

Mixture break is one of the pipe organ playing techniques to add more

stops, which is a group of pipes, for the notes in lower pitches. By using more

pipes in lower pitches, the level of lower note is increased and tonal balance

can be adjusted.

This method is adapted to engine Sound #1 to adjust tonal balance in

higher RPM by adding a lower order as presented in Table 6.7. C0 is used as

notes for break back in the table. The level of C0, which is 1.5th order, only

increased from 3500 RPM to 5000 RPM. This increase adjusts the tonal

balance between the high frequency band and low frequency band.

Figure 6.3 shows an FFT analysis of engine Sound #3. It is found that the

level of 1.5th order is increased in higher RPM compared to the original

engine sound in Figure 6.5(a).

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Table 6.7 Application of Break Back Method (Engine Sound #1)

Note Order Frequency(Hz)

2000 RPM 3500 RPM 5000 RPM

C0 1.5 87.50 125.00

C1 3 100.00 175.00 250.00

G1 4.5 150.00 262.50 375.00

C2 6 200.00 350.00 500.00

E2 7 233.33 408.33 583.33

G2 9 300.00 525.00 750.00

C3 12 400.00 700.00 1,000.00

E3 15 500.00 875.00 1,250.00

Figure 6.3 FFT Analysis of Engine Sound #1 (6 Cylinders)

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6.4.4 Application of Formant Filter

From a tiger’s two roaring samples introduced in Chapter 5, where one is for

Vigilant condition and the other is for Onslaught condition, two formant filter

estimations were conducted. Based on the values of estimation, two formant

filters were built by four IIR filters with different settings. Figure 6.4 shows

the formant filters generated using IIR filters of the HEAD ACOUSTICS

software tool. These two formant filters were used for engine Sound #3.

Formant filter 1 is used for the engine sound synthesis with the range of

1000–3500 RPM and Formant filter 2 was used for the range of 3500–6000

RPM. Table 6.8 presents the frequency settings for the two formant filters.

Figure 6.4 Formant Filters for Sound #3 Synthesis

(a) Formant filter with Vigilant Concept for

Lower RPM (1000–3500 RPM)

(b) Formant Filter with Onslaught Concept for

Higher RPM (3500–6000 RPM)

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Table 6.8 Settings for 2 Formant Filters

(a) Formant Filter 1 Settings for Low RPM (1000–3500 RPM)

Formant F1 F2 F3 F4

Frequency (Hz) 80 250 500 700

Gain (dB) 2 1 3 4

Q value 1 1 1 1

(b) Formant Filter 2 Settings for High RPM (3500–6000 RPM)

Formant F1 F2 F3 F4

Frequency (Hz) 100 200 500 720

Gain (dB) 4 1 2 3

Q value 0.5 0.5 0.5 0.5

6.4.5 Result of Engine Sound Synthesis

Figure 6.5 shows an FFT analysis of Engine Sound #3, to which formant

filter was applied. It is found that SPLs on formant frequencies are increased

in Figure 6.5(b).

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(a) FFT analysis for Original Engine Sound

(b) FFT Analysis for Synthesized Engine Sound with Formant Filter

Figure 6.5 FFT Analysis for Engine Sound #3 (6 Cylinders)

Figure 6.6 and 6.7 show the results of an FFT analysis with a waterfall

view for the original engine and synthesized engine sounds, which are

119


recorded at driver’s position with WOT condition (4-cylinder and 6-cylinder

engines). It is found that levels of different orders are increased depending on

engine sounds.

Figure 6.6 FFT Analysis (Water Fall) of Original Engine Sound and Synthesized Engine Sounds (4 Cylinders)

(a) Original 4-Cylinder Engine Sound

(b) Engine Sound #1 (4 Cylinders)

(c) Engine Sound #2 (4 Cylinders)

(d) Engine Sound #3 (4 Cylinders)

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Figure 6.7 FFT Analysis (Water Fall) of Original Engine Sound and

Synthesized Engine Sounds (6 Cylinders)

(a) Original 6-Cylinder Engine Sound

(b) Engine Sound #1 (6 Cylinders)

(c) Engine Sound #2 (6 Cylinders)

(d) Engine Sound #3 (6 Cylinders)

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6.5 Test and Validation

6.5.1 ASD System Test

ASD system tests were conducted for each main order of 4-cylinder (2nd

order) and 6-cylinder (3rd order) cars. This is the first stage of Part 4, Test

and Validation, and the functionality of ASD system is tested in this stage.

The order level of target engine sound design and synthesized engine

sound using the ASD system in a car were compared following the directions

in the, ASD system test stage for validation of the process. For this, three

proposed parameters for new engine sound design were applied to the ASD

system one by one, and the sounds with these three setting were recorded

separately in two cars with WOT conditions. The target order level and

synthesized engine order level were measured using the HEAD ACOUSTICS

software measurement tool at 2000, 2250, 2500, 2750, 3000, 3250, 3500,

3750, 4000, 4250, 4500, 4750, and 5000 RPM for the test.

Table 6.9 presents the result of the order level (dB SPL) measurement for

the 4-cylinder engine sound whereas Table 6.10 provides the result of the

order level measurement (dB SPL) for the 6-cylinder engine sound. The

result shows that order levels of synthesized engine sounds are tracking the

target orders by 3 dB variance except for Sound #1. In the case of Sound #1,

the difference between the target order level and synthesized order level was

4.75 as indicated in Table 6.9(a).

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Table 6.9 Result of Order Level (dB SPL) Measurement (4 Cylinders)

(a) Sound #1 (4 Cylinders) RPM Original Level Target Level Synthesis_1 Difference

2000 61.24 71.24 75.99 4.75 2250 67.85 75.85 77.01 1.16 2500 75.45 81.45 81.35 -0.1 2750 82.88 88.88 87.34 -1.54 3000 80.86 86.86 85.63 -1.23 3250 76.87 84.87 88.57 3.7 3500 79.36 79.36 80.18 0.82 3750 79.35 79.35 77.24 -2.11 4000 77.92 82.92 84.6 1.68 4250 77.55 81.55 83.04 1.49 4500 77.32 80.32 78.85 -1.47 4750 79.95 82.95 84.75 1.8 5000 77.27 77.27 76.5 -0.77

(b) Sound #2 (4 Cylinders) RPM Original Level Target Level Synthesis_2 Difference

2000 61.24 77.24 80 2.76 2250 67.85 81.85 81.05 -0.8 2500 75.45 82.45 82.04 -0.41 2750 82.88 82.88 80.11 -2.77 3000 80.86 85.86 86.8 0.94 3250 76.87 83.87 84.89 1.02 3500 79.36 87.36 87.18 -0.18 3750 79.35 87.35 86.87 -0.48 4000 77.92 77.92 77.74 -0.18 4250 77.55 77.55 78.86 1.31 4500 77.32 80.32 80.71 0.39 4750 79.95 79.95 79.77 -0.18 5000 77.27 84.27 83.92 -0.35

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(c) Sound #3 (4 Cylinders) RPM Original Level Target Level Synthesis_3 Difference

2000 61.24 86.24 84.65 -1.59 2250 67.85 82.85 83.49 0.64 2500 75.45 85.45 87 1.55 2750 82.88 82.88 81.15 -1.73 3000 80.86 86.86 88.78 1.92 3250 76.87 86.87 89.21 2.34 3500 79.36 89.36 91.36 2 3750 79.35 84.35 83.67 -0.68 4000 77.92 87.92 88.39 0.47 4250 77.55 83.55 83.14 -0.41 4500 77.32 84.32 83.5 -0.82 4750 79.95 79.95 78.15 -1.8 5000 77.27 87.27 86.55 -0.72

Table 6.10 Result of Order Level (dB SPL) Measurement (6 Cylinders)

(a) Sound #1 (6 Cylinders) RPM Original Level Target Level Synthesis_1 Difference

2000 81.16 90.16 90.05 -0.11 2250 81.48 88.48 88.41 -0.07 2500 71.92 77.92 77.8 -0.12 2750 72.93 78.93 79.13 0.2 3000 72.98 75.98 74.44 -1.54 3250 76.04 82.04 83.8 1.76 3500 64.4 84.4 86.94 2.54 3750 60.45 85.45 87.85 2.4 4000 68.58 83.58 81.83 -1.75 4250 65.22 75.22 77.25 2.03 4500 60.1 75.1 77.43 2.33 4750 61.11 71.11 69.09 -2.02 5000 66.44 76.44 74.57 -1.87

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(b) Sound #2 (6 Cylinders) RPM Original Level Target Level Synthesis_2 Difference

2000 81.16 91.16 89.69 -1.47 2250 81.48 91.48 91.07 -0.41 2500 71.92 81.92 84.14 2.22 2750 72.93 81.93 81.02 -0.91 3000 72.98 81.98 81.57 -0.41 3250 76.04 81.04 79.17 -1.87 3500 64.4 89.4 89.86 0.46 3750 60.45 90.45 88.54 -1.91 4000 68.58 83.58 82.14 -1.44 4250 65.22 80.22 79.82 -0.4 4500 60.1 80.1 78.53 -1.57 4750 61.11 66.11 69.06 2.95 5000 66.44 71.44 74.04 2.6

(c) Sound #3 (6 Cylinders) RPM Original Level Target Level Synthesis_3 Difference

2000 81.16 81.16 81.9 0.74 2250 81.48 81.48 79.34 -2.14 2500 71.92 77.92 78.43 0.51 2750 72.93 78.93 77.98 -0.95 3000 72.98 78.98 77.05 -1.93 3250 76.04 76.04 73.58 -2.46 3500 64.4 79.4 78.94 -0.46 3750 60.45 80.45 80.01 -0.44 4000 68.58 73.58 71.9 -1.68 4250 65.22 70.22 71.2 0.98 4500 60.1 70.1 70.36 0.26 4750 61.11 71.11 71.05 -0.06 5000 66.44 66.44 64.75 -1.69

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dB SPL

dB SPL

Figure 6.8 and 6.9 show a graphical comparison between target order levels

and synthesized order levels for the 4-cylinder engine and 6-cylinder engine

sounds. In general, the results are in agreement with the results of study by

Lee et al. (2016).

(a) Sound #1 (4 Cylinders)

(b) Sound #2 (4 Cylinders)

RPM

RPM

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dB SPL

dB SPL

(c) Sound #3 (4 Cylinders)

Figure 6.8 Result of Order Level Comparison (4 Cylinders)

(a) Sound #1 (6 Cylinders)

RPM

RPM

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dB SPL

dB SPL

(b) Sound #2 (6 Cylinders)

(c) Sound #3 (6 Cylinders)

Figure 6.9 Result of Order Level Comparison (6 Cylinders)

RPM

RPM

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6.5.2 Position Matching Test

Following the directions of the position matching test stage, the positions

of 8 engine sounds on the positioning map were calculated using the

regression equations derived in this study as discussed in Chapter 4. In

general, the positions of new engine sounds are moved to new positions,

which are close to the target position except for Engine Sound #1 (6

Cylinders). In the case of Engine Sound #1 (6 Cylinders), the levels of

Refined and Powerful have been increased more than expected. These levels

were adjusted based on the test in this stage. Table 6.11 presents the result of

Refined and Powerful calculation based on the acoustic parameter

measurement for synthesized engine sounds. Figure 6.10 shows the result of

the position matching test for new engine sounds.

Table 6.11 Refined and Powerful Levels Calculated from Acoustic Parameter Measured from Synthesized Engine Sounds

(a) 4 Cylinders

Original Sound #1 Sound #2 Sound #3

Refined 4.62 4.74 4.74 4.83

Powerful 4.89 5.47 5.39 5.54

(b) 6 Cylinders Original Sound #1 Sound #2 Sound #3

Refined 4.41 4.72 4.52 4.58

Powerful 5.15 6.92 5.99 5.93

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(a) 4 Cylinders

(b) 6 Cylinders

Figure 6.10 Result of Position Matching Test

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6.5.3 Operational Test

The operational test for eight engine sound designs, including the

original engine sounds, were performed in the two target cars with 4-cylinder

engine and 6-cylinder engine, with acceleration condition following the

directions of the operational test stage. Six synthesized engine sounds were

generated by the ASD system, controlled by the ASD tuning PC, which has

all ASD tuning data for synthesized engine sounds.

Two NVH experts and one sound design expert participated in the test to

evaluate six synthesized engine sounds in the target vehicle. All engine

sounds were tested one by one after the dedicated ASD tuning data are loaded

into the ASD system. The test was performed at the track with WOT

conditions and recorded with the head set recorder at the same time.

In general, all sounds were in acceptable level for the target Powerful

and Refined positions, and the concepts of each engine sounds were well

defined. However, small adjustments were required to keep a good balance

between the real engine sound and artificial sound, which are generated from

the ASD system, especially for the 4-cylinder engine.

These comments were applied to the final engine sound designs, and

the level of sound from the ASD system was adjusted by changing the setting.

Table 6.12 provides the result of the operational test for the 4-cylinder engine

including the adjusted level based on the feedback from participants.

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Table 6.12 Example of Operational Test for Overall Order Level (4 Cylinders)

(a) Engine Sound #1

Note C0 C1 E1 G1 C2 E2 G2 C3 E3 G3

Order 1 2 2.5 3 4 5 6 8 10 12

Adjusted Level(dB) -3 -2 0 0 0 +3 +6 +6 +6 off

(b) Engine Sound #2

Note C1 E1 G1 C2 E2 G2 C3

Order 2 2.5 3 4 5 6 8

Adjusted Level(dB) -2 -2 -1 -1 +3.5 +8.5 +8.5

(c) Engine Sound #3

Note C1 E1 G1 F C2 E2 G2 C3

Order 2 2.5 3 3.5 4 5 5.5 6

Adjusted Level(dB) +3 +3 +3 +3 +3 +3 +3 +3

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6.5.4 Verbal Description

The standardized verbal description and technical description for the 6-

cylinder engine were conducted based on the directions in Part 5, Verbal

Description. Table 6.13 presents the standardized verbal description for the

eight engine sounds with two representative affective adjectives, Refined and

Powerful. Table 6.14 provides the technical description of engine sounds.

Table 6.13 Standardized Verbal Descriptions for 8 Engine Sounds

Name Engine Sound #1 Engine Sound #2 Engine Sound #3

Synthesis Method

Mixture Break Back

Beat

Musical Harmonic Theory Formant Filter

Cylinder

4 Prime, Major 3rd, Perfect 5th, Octave



Perfect 4th

6 Prime, Major 3rd,

Perfect 5th, OctaveMinor 3rd


Perfect 4th

Prime, Major 3rd, Perfect 5th, Octave Major 2nd, Perfect

4th

Concept Pipe Organ with

Mixture Musical Harmony Tiger Roaring

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Table 6.14 Technical Descriptions of Engine Sounds

(a) Technical Description for 4 Cylinders (2000-5000 RPM)

Item 4-CylinderOriginal

4-Cylinder#1

4-Cylinder #2

4-Cylinder #3

Sharpness (acum) 0.95 0.95 0.94 0.93

AwSPL (dB) 85.5 80.0 82.9 81.5

Loudness (sone) 22.0 27.8 26.4 28.9

Tonality (tu) 0.0696 0.102 0.115 0.0925

(b) Technical Description for 6 Cylinders (2000-5000 RPM)

Item 6-Cylinder Original

6-Cylinder #1

6-Cylinder #2

6-Cylinder #3

Sharpness (acum) 1.04 1.04 1.04 1.03

AwSPL (dB) 77.8 65.2 73.4 72.7

Loudness (sone) 24.9 38.3 32.3 34.3

Tonality (tu) 0.0764 0.3 0.152 0.0702

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6.6 Discussions

Following the directions in Part 3, Engine Sound Design, of the sonic

branding process for ASD proposed in this study, real engine sounds were

synthesized in as explained in this chapter. Based on the original engine

sound of two target vehicles, i.e., 4-cylinder and 6-cylinder engines, three

new engine sounds were developed for each target vehicle. The target

position for new engine sounds in the two target vehicles were defined on the

Refined and Powerful positioning map, and acoustic parameters for these

positions were proposed by regression equations in the define target position

stage.

In the prototype engine sound design stage, new synthesis methods and the

proposed methods in this study, namely musical harmonic theory, formant

filter, and pipe organ mixture break back, were used for engine sound

development in cooperation with a sound design expert. Three different

engine sounds for the 4- cylinder and 6-cylinder engines were synthesized

with different concepts, which are composed of new synthesis methods.

Then the test and validation, which is Part 4 of the process, was conducted.

The main orders of synthesized new engine sounds were measured and tested

to check its tracking trend for the target order level, which should be

performed at the ASD system test stage. Acoustic parameters of new engine

sounds were measured as well, for representative adjectives level calculation.

Then, the characters of new engine sounds were tested on the positioning

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map to check if the new positions match with the target positions in the

position matching test stage.

All new engine sound designs were applied to the ASD system in target

vehicles and tested at test track by NVH experts and a sound design expert in

WOT condition for the operational test stage. In general, new engine sounds

were acceptable and the level of virtual engine sounds from the ASD system

were adjusted slightly for a better balance between real engine sounds and

synthesized engine sounds.

Finally, the verbal description, which is Part 5 of the process, was

conducted for the standardized verbal description and technical description of

the eight engine sounds including the original ones.

The validity of sonic branding process was tested as discussed in this

chapter. The result shows that the process can be used for real ASD

development and the position of new engine sound can be moved to a new

one as intended following the process and guideline. Collaboration with a

sound design expert, who has insights for engine sound design, is

recommended to apply the new sound synthesis proposed in this study.

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CHAPTER 7. DISCUSSION AND CONCLUSION

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7.1 Summary of Findings

Car makers are trying to represent their brand identities and images using

the ASD system recently. ASD is a software solution, which can be applied to

current infotainment systems without changing the hardware in a car. As the

differences in hardware performances depending on the brands are less than

those before, the interest for ASD is increasing in the automotive industry as

a key method for improving the affective quality of engine sound. However,

there are few studies on affective engine sound development for ASD, which

can be applied to improving brand identity with engine sound in real

conditions.

In this study, a sonic branding process is proposed, by which engine sound

can be developed for a target brand identity and image of car class efficiently

with the ASD system in the vehicle. Subjective rating, interviews, and

questionnaires, which are subjective measurement methods for affective

engineering, were used to propose methods for evaluating and designing

engine sound. A regression model for engine sound was built based on the

results of the statistical analysis. Then engine sound designs were conducted

for target cars following the process. In addition, the test and validation of the

process was performed for the new engine sounds in real driving conditions.

First, a sonic branding process is proposed for engine sound

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development with the ASD system, which can adjust the sound characters of

the original engine to new ones to improve brand identity. Considering that

engine sound is one of the AUIs in automobiles, previous studies on AUI

design process were adapted. The process is composed of five parts, namely

regression model for engine sound, defining target engine sound, engine

sound design, engine sound test, and verbal description.

Part 1, Regression Model for Engine Sound, consists of 6 stages, namely

establishing the need for sound identity for a given referent brand, adjectives

for engine sound, acoustic parameters for engine sound, jury test, factor

analysis, and regression analysis. Representative adjectives are extracted

during the regression analysis stage by factor analysis. Then regression

equations are applied in the process for the regression model for engine

sound stage.

Part 2 is Brand and Synthesis Method, which consists of defining target

position and synthesis method selection. In defining target position, the

position of target engine sound on the two-dimensional positioning map, is

defined based on brand identity and car class of target car. Depending on the

concept of target engine sound, the synthesis method can be decided at the

synthesis method selection stage.

Part 3, which is Engine Sound Design, consists of only one stage,

prototype engine sound design. Collaboration with a sound design expert,

who has insights for sound design, is recommended for engine sound design

at this stage. The engine sound designs are applied to the ASD system in the

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target cars with 4 cylinders and 6 cylinders and new engine sounds are

presented through the ASD system.

Part 4, Test and Validation, consists of the ASD system test, position

matching test, and operational test stages. The ASD system test stage is for

functionality test of the ASD system to ensure that the ASD system is

working properly with the parameters that have been set. At positioning

matching test stage, the position of the new engine sound is compared with

the target position, which has been defined before the development. The

operational test is the stage at which a listening test is performed in driving

condition.

Part 5, Verbal Description, consists of the standardized verbal description,

which is last stage of the process. General information of engine sound,

including sound concept and synthesis methods, are described for each

engine sound. Technical description is part of verbal description and technical

information, including acoustic parameters, are presented in this stage.

Second, the relationship between affective adjectives for engine sound

characters and acoustical parameters on engine sound was analyzed with

statistical methods to build the regression model for engine sound. For this,

subjective rating, interviews, and questionnaires, which are subjective

measurement methods, were used to build the regression model for engine

sound.

A literature review and an expert interview were conducted to build pools

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140

for affective adjectives and acoustic parameters, and statistical analysis was

conducted to find representative adjectives and causal relationships between

affective adjectives and acoustic parameters, based on the result of jury test.

The jury test was conducted to evaluate the level of nine affective

adjectives, which were selected from previous studies on engine sound

quality evaluation, for each engine sound recordings. Engine sounds of 38

cars, which are composed of compact, luxury, and sporty cars, were recorded

in WOT condition and evaluated by 42 participants.

A factor analysis was performed for the result of the jury test to find

factors for the nine adjectives and group them. Then two representative

adjectives Refined and Powerful were obtained from the factor analysis, and

they are used as dependent variables for the regression equations.

Psychoacoustic parameters, which were chosen from the literature review

and interview with experts, were measured by a measurement software tool.

Additional acoustic parameters, proposed in this study, were measured as

well. All measurements were performed for the range 2,000–5,000 RPM.

Then correlation and regression analyses were conducted for the two

representative adjectives and the measurement result of psychoacoustic

parameters and additional acoustic parameters. Then the regression model for

engine sound was built based on the result of the statistical analysis, by which

brand identity and car class can be represented.

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141

In addition, the positioning map with two-dimensional grid, which has

been used previous studies, was proposed as a visual tool for engine sound

evaluation. The positioning map, which has two axes, Refined and Powerful,

is useful not only for evaluating current engine sound, but also for defining

sound identity of future engine sound.

Third, novel engine sound synthesis methods are introduced and new

engine sound synthesis methods are proposed in this study. Recently, various

engine sound synthesis methods are studied by car manufacturers to improve

the affective quality of cars with engine sounds. There were some attempts to

adapt music composition theories to engine sound development considering

that the structure of musical harmonics is similar to that of the engine order.

Biomimetic methods, for example, using formant filter, which imitates the

cavity structure, are studied in some previous studies by which unique sound

characters can be presented. In addition, engine sound level linearization

depending on RPM increase is one of the topics that car makers are interested

in. A previous study showed that linear sound level increase depending on

RPM is related to engine sound quality.

In the meantime, new synthesis methods are proposed in this study.

Acoustic characteristics of a pipe organ, of which the structures are similar to

those of the intake and exhaust systems, can be applied to engine sound

synthesis. For this, a real pipe organ recoding sample was analyzed to adapt

the harmonic structure of pipe organ sound to the order structure of engine

sound. In this context, the mixture break back method can be adapted to

lower orders in higher RPM to provide more dynamic perception to drivers

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and adjust the balance between high frequency and low frequency in higher

RPM. Additionally, beat effect, which occurs in two pipes with small

differences in length, can be used to reproduce vibrations in low frequencies

generated by the exhaust system.

Fourth, new engine sounds were developed on the ASD system in two

target cars with 4- cylinder and 6-cylinder engines following the sonic

branding process proposed in this study, to validate the process. The two

target cars were equipped with ASD systems in production, which can be

controlled by an ASD tuning software installed on a PC and three engine

sounds were designed for each target car.

The target position of Refined and Powerful, on the positioning map, were

defined to improve the level of Refined and Powerful, and target ranges for

design acoustic parameters, which resulted in the target level of affective

adjectives using the regression model for engine sound, were obtained. The

boundaries of target ranges were marked on the positioning map. Then engine

sound synthesis methods were selected to make three engine sounds with

different concepts for each target car.

Based on the target positioning and selected synthesis methods, engine

order levels were obtained to which synthesis methods have been

implemented by using a simulation software tool. Engine sound was designed

in cooperation with a sound expert, who has insights for sound design.

The final results of six engine sound designs were applied to the ASD

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143

system, which was equipped in external amplifiers, in the two target cars and

new engine sounds were synthesized by the ASD system.

The validity of the new engine sound was checked following the test and

validation part of the sonic branding process. In the test and validation

process, the acoustic parameters of six new engine sounds were measured to

calculate level of Refined and Powerful for the new engine sounds and those

were compared with the target positions, which have been defined at the

beginning of the process.

The result of ASD system test shows that it works properly and the main

order levels depend on RPM track target order levels within 3 dB variances

as intended. It means that each new engine sound design for the target cars

was applied to the ASD system properly. This method was very helpful to

check if the ASD system works as efficiently as it should.

The result of position matching test shows that most of the positions of

new engine sounds were in the range of the target position on the Refined and

Powerful positioning map as planned. Five positions from six positions of

new engine sounds were in the boundaries of target positions. Even though

there was an engine sound, which positioned out of the target range on the

positioning map, the trends of all new engine sounds on the positioning were

in the same directions as intended.

Finally, the operational test for the six new engine sounds was performed

in driving condition on the test track. Two NVH experts and one sound

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design expert joined the test. The sound level balance between the original

engine sound and artificial engine sound from the ASD system was checked

during the test and the sound levels from the ASD system were adjusted

based on the result of the test.

After all tests were completed, the standardized verbal description and

technical description were prepared for the new engine sounds following the

verbal description part of the sonic branding process.

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7.2 Contribution of This Study

There are few studies on affective engine sound development for ASD,

which can be applied to improving brand identity with engine sound in real

conditions. Previous studies on product development process are for visual

interfaces. Without a guideline for engine sound development for ASD, the

development time can be increased, and the possibility of success for the

development will be decreased.

The result of this study can be used to develop engine sound for ASD using

the sonic branding process, with which affective quality including brand

identity and car class image can be evaluated and improved. Efficient and

reliable engine sound design will be achieved by following the process,

which is built based on previous studies on AUI and affective engineering

methods. Development time can be reduced and the possibility of success for

engine sound design will be increased.

In addition, the regression model for engine sound, built in this study, will

be helpful to estimate the character of new engine sound for ASD system in

advance. Estimating a new engine sound character will contribute to a proper

engine sound design and efficient brand identity strategy using engine sound.

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7.3 Limitations and Future Work

To increase the value of R2 of regression equations, additional acoustic

parameters were proposed. By using additional acoustic parameters, the value

of R2 of the regression model for engine sound was increased. However,

because of the measurement process for additional acoustic parameters,

psychoacoustic parameters were used for engine sound development in the

end. More studies on acoustic parameters by which the value of R2 can be

increased and which can be measured easily must be conducted.

Regarding the jury test, which was performed to build the regression

equations, only participants who had Korean nationality participated. There

are previous studies that show different preferences for engine sound

depending on regions. Therefore, a global jury test by participants from each

region is necessary to build regression equations, which can be used globally.

Moreover, luxury car owners and sports car owners were excluded to avoid

any bias on the result based on previous studies. However, at the same time,

they are known to be passionate to answer questions on engine sound

evaluation. Because their feedback can be recognized as dominant customer

opinion on consumer report in the real market, it is necessary to conduct

additional jury test for luxury and sports car owners to understand various

customer needs.

In addition, Hybrid/EVs were excluded as target vehicles for the jury test

and application. Of course, the engine sound process, proposed in this study,

can be applied to engine sound development for Hybrid/EVs, as the approach

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147

is based on human perceptions for sound. However, the result of previous

research shows that normal people expect a little different concept for

Hybrid/EVs. Therefore, additional jury tests for Hybrid/EVs will be

necessary in future studies to build up additional regression equations that are

dedicated to Hybrid/EVs.

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ed.). Berlin, Gemany: Springer-Verlag.

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Appendix A. Questionnaire for Jury Test

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Appendix B. Result of Parameter Measurement

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Appendix C. Result of FFT Analysis of Engine Sound Samples (FFT CUT, 3500 RPM)

A-1 A-2

A-3 A-4

A-5 B-1

B-2 B-3

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B-4 B-5

B-6 C-1

C-2 C-3

C-4 D-1

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D-2 D-3

D-4 D-5

D-6 D-7

D-8 E-1

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E-2 E-3

E-5 F-4

G-1 G-2

H-1 I-1

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J-1 K-1

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국문 초록

ABSTRACT (KOREAN)

“소닉 브랜딩”은 청각을 통하여 브랜드의 정체성을 전달하는 방법으로, 자

동차에서 발생하는 전장음, 엔진음, 동작음 등을 모두 하나의 “Auditory User

Interface (AUI)”의 영역으로 분류하고 자동차의 모든 AUI에 “소닉 브랜딩”

방법을 적용하기 위한 연구가 이루어지고 있다. 이러한 맥락에서, 자동차 제

조사들은 엔진음을 통하여 그들의 브랜드 이미지를 표현하고자 지난 수십년

간 흡기계와 배기계의 하드웨어를 개선하려는 노력을 해오고 있다.

최근에는 기존의 인포테인먼트 시스템에 소프트웨어만을 추가함으로써 엔

진음을 개선할 수 있는 “Active Sound Design (ASD, 가상엔진사운드)” 시스

템을 이용하여 브랜드 정체성을 표현하고자 하는 시도가 이루어지고 있다. 독

일, 프랑스, 영국 소재의 소프트웨어 개발업체들이 이미 ASD 상용 솔루션을

앞다투어 출시하고 있고, 출시된 소프트웨어의 완성도가 실제 적용 가능한 수

준으로 높아져 있는 상태이다. 브랜드별 하드웨어적인 자동차 성능의 편차가

점점 줄어들고 있는 상황에서, 자동차의 중요한 AUI 중의 하나인 엔진음을

통하여 감성품질을 높이고 브랜드 정체성을 표현하기 위한 방법으로써 ASD

시스템에 대한 관심은 전세계적으로 높아지고 있다. 그러나 기존의 ASD 시

스템 관련 연구는 ASD 알고리듬의 개발에 대한 내용이 대부분이며 ASD 시

스템을 활용한 엔진음 제작 프로세스 관련 연구는 아직 미비한 상황이다. 또

한, 이제 ASD 시스템을 도입하려고 하는 자동차 제조사들은 ASD 기술을 통

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하여 브랜드 정체성을 표현할 수 있는 방법론을 확보하는 데에 어려움을 겪

는 것으로 알려져 있다.

본 연구에서는 본래 엔진음이 가지고 있는 음향학적 특성을 기반으로 하여,

현재 엔진음의 브랜드 정체성 및 이미지를 평가하고 이를 바탕으로 엔진음에

“소닉 브랜딩” 방법을 효율적으로 적용할 수 있는 “소닉 브랜딩 프로세스”를

개발하였다. 전체적인 개발 프로세스는 프로세스의 신뢰성을 확보하기 위하여

기존의 AUI 제작 프로세스 관련 연구를 기반으로 하여 구축하였고, 엔진음의

평가와 제작을 위한 방법론은 감성공학의 주관적 평가 방법들 중 “Subjective

Rating”, “Interviews”, “Questionnaire” 및 이들의 결과에 대한 통계적 분석

등이 사용되었다. 또한, 엔진음 평가와 제작의 효율성을 위하여 음향 파라메

터와 감성형용사 간의 인과관계를 보여주는 “Regression Model for Engine

Sound “가 도출되었다.

이러한 제안을 위하여 첫번째로, 기존 엔진음의 감성형용사의 수준을 분석

하여 이를 기존 연구에서 엔진음 분석에 주로 활용되는 2차원 포지셔닝 맵에

서 평가하고, 음향 파라메터와 감성 형용사의 인과관계를 보여주는 회귀식을

통하여 목표로 하는 포지션으로 엔진음의 감성 어휘 수준을 조정함으로써 엔

진음의 브랜드 정체정을 개선할 수 있는 “소닉 브랜딩 프로세스”를 구축하였

다. 두 번째로, “소닉 브랜딩 프로세스”에서 필요한 회귀식을 제시하기 위하

여 청감 실험을 실시하고 이에 대한 결과를 통계적으로 분석하여, 브랜드 정

체성 및 차급의 수준에 대한 언어적 표현과 엔진음이 가지고 있는 음향적 특

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성의 인과관계에 대하여 연구하였다. 세번째로 ASD 기술에 적용 가능한 새

로운 감성 엔진음 합성 방법론들이 제시되었다. 최근 자동차 제조사들 사이에

서 시도되고 있는 “Musical Harmonic Theory”, “Formant Filter” 등의 기법

들과 본 연구에서 새롭게 제시되는 파이프 오르간의 “Mixture Break Back”,

“Beat Effect” 등이 함께 소개되었다. 마지막으로, 제시된 소닉 브랜딩 프로세

스의 유효성을 검증하기 위하여 실제 ASD 시스템에서 감성 엔진음 제작 프

로세스를 기반으로 한 새로운 엔진음을 개발하였으며, 엔진음 제작의 과정에

는 사운드 디자인에 통찰력을 가진 사운드 디자이너가 참여하였다. 제작된 총

6개의 엔진음 중 5개의 엔진음이 목표로 하는 포지셔닝 맵의 범위에서 확인

되었으며, 범위를 벗어난 1개의 엔진음도 목표로 하는 방향에는 부합하는 방

향으로 포지션이 개선되었다.

본 연구의 결과를 바탕으로, ASD를 활용한 엔진음의 “소닉 브랜딩”을 구현

할 수 있을 것이다. 특히 본 연구에서 제시된 “소닉 브랜딩 프로세스”를 통하

여, ASD 개발기간을 단축시키고 개발의 성공확률을 높임으로써 효율적이고

신뢰성 높은 결과물을 제작할 수 있을 것이다. 또한, 본연구에서 도출된

“Regression Model for Engine Sound” 를 통하여 ASD 시스템을 사용하여 새

로운 엔진음을 제작할 때에 엔진음의 캐릭터를 사전에 예측할 수 있다. 엔진

음의 캐릭터를 사전에 예측할 수 있음으로 인하여, 엔진음을 통한 브랜드 아

이덴티티 구현이 효율적으로 이루어지게 될 것이다.

참고로, 본 연구에서는 대한민국의 일반차량 소유자만을 대상으로 연구가

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진행되었다. 본 연구의 내용이 글로벌 하게 적용되기 위해서는 추후 국적 및

소유 차종 등을 고려한 연구가 필요하다. 또한 본 연구를 바탕으로 한 전기자

동차 및 하이브리드 자동차의 엔진음에 대한 소닉 브랜딩도 의미 있는 추후

의 연구 주제가 될 것이다.

주요어 : 소닉브랜딩(Sonic Branding), 감성공학(Affective Engineering), 능동음

향설계(Active Sound Design), 엔진음설계(Engine Sound Design), 사운드합성

(Sound Synthesis)

학 번 : 2012-30285

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