A Study on Noise Reduction in an Enclosed Space by Destructive Interference

By Lt Col (Dr) RM Monaragala

Maj KVP Dhammika Flt Lt EPDR Edirisinghe

Content

• Background

• Aim

• ANC Principles

• Literature Review

• Practical Experiment and Results

• Future Applications

• Conclusion

• Questions

Background

Research on noise control has become important

Exposure to the noise may result in deafness and cardiovascular complaints.

Acute effects of noise include changes in performance efficiency.

Noise Sources

Air Condition fan, aircraft propeller noises fall below 500 Hz

Interior Noises

• Propeller Noise: Inside an aircraft. – Single low frequency, around 200 to 400 Hz.

• Engine Noise: Inside an automobile. – Single low frequency , around 200 to 300 Hz.

• Road Noise: Inside an automobile. – Wide band low frequency noise from tires on road

– Range from 100 Hz to 500 Hz.

Interior Noises

• Aerodynamic Noise: Wind noise – High frequency noise above 1000 Hz.

– Caused by wind sweeping through partly openings to the vehicle interior

• Air Condition fan Noise – Passes through air condition ducts to an enclosed space

• Structure borne vibrations – Low frequency noise.

Active Noise Control (ANC)

Primary Noise Waveform Anti Noise Waveform

Residual Noise

Aim

To develop Active Noise Controller to reduce low frequency noise and use for the defence

applications.

Importance of Reducing Interior Noise Level

• Minimize the fatigue on driver & passengers in an automobile

• Improve the audio environment (for music)

• Improve the comfort of passengers

• Make the class rooms Quiet

ANC Principles

Noise

• Broadband Noise

- Ex: Low-frequency sound of a jet plane, the impulse noise of a gunshot.

• Narrowband Noise

- Ex: The noise caused by a car’s combustion engine.

Noise Reduction Methods

• Active Method

– Effective to reduce Noise up to 500 Hz.

• Passive Method

– Effective to reduce Noise beyond 500 Hz.

Passive Methods

• Sound absorption

• Sound isolation

• Vibration isolation

• Vibration damping

• Mufflers

An ANC system can be effective across the entire noise spectrum, but it is particularly

appropriate at low frequencies of up to 500 Hz, where passive systems are less effective.

Destructive Interference

ANC is developing rapidly due to improvements in noise control, often with potential benefits

in size, weight, volume, and cost.

Different Kinds of ANC

ANC systems are divided into three different groups.

• Global free space cancellation

• Zone-of-silence cancellation

• Cavity and duct cancellation

Feed Back ANC System

Feed Forward ANC System

Narrowband Feed Forward ANC System

Destructive Interference

The resulting sound is null, the sound energy is transformed into heat

The net result is no sound at all.

Literature Review

A microphone & a loudspeaker to generate a canceling sound was first proposed & patented

by Lueg in 1936.

Burgess developed a duct-noise cancellation system based on adaptive filter theory in 1981.

Later in the 1980s, Digital Signal Processing was Developed

The specialized DSPs were designed for real-time numerical processing of digitized signals.

Adaptive Filters – LMS Algorithm

Adaptive Filters

• The principle is to calculate an output that is equal to the unwanted disturbance by minimizing the error signal.

• The disturbance will then be cancelled by superposition of the inverted output of the filter on it.

ANC Controller

• The ANC Controller is a Finite Impulse Response Filter. • It computes the anti phase audio signal using a Least

Mean Square Error algorithm.

LMS Algorithm

• Output Signal Calculated as

• Primary Signal (input to ANC)

• Weight vector at time n

)1(..)1()()( Nnxnxnxnx

TN

T nwnwnwnw )(..)()()( 110

1

0

)()()(N

i

i inxnwny

LMS Algorithm

• Then the output signal y(n) in equation can be expressed by the vector operation

• The error (difference in phase between the primary and secondary) can be written as

)()()()( ndnxnwne T

)()()()()(1

0

inxnwnxnwnyN

i

i

T

Optimization

• Objective of the LMS algorithm is to find the optimal weight vector where, the mean square value of the error signal e(n) is minimized.

• This results in the weight vector to be updated as:

)().()()1( nenxnwnw

)()( nxnxT

20

Practical Experiment and Results

ANC Simulation Model

The concept was simulated using Labview based on the following model:

Initial ANC Experimental Model With Labview

ANC Simulation With Labview

Wide band Noise Spectrum without ANC

Wideband Noise Spectrum with ANC

PSD of Noise at Error Microphone Without Control

PSD of Noise at Error Microphone with Control using

the LMS Algorithm

Initial Test Results Obtained With JAVA Experimental Model

Initial Test Results Obtained With JAVA Experimental Model

Future Applications

ANC Application In Defence

• Aid to reduce aircraft interior noise

• Aid to reduce engine vibration noise in cabins of ships

• Reduce engine boom in a tank interior

• Reduce the Air-condition fan noise inside the Classroom

Future Plans

• Develop the same model to obtain more filtered effect.

• Develop the circuitry for the use of opened space to reduce environmental noise.

• Develop the circuitry for the use of airfield to reduce jet aircraft engine noise.

Conclusion

• The different control methods available today for active noise control were studied.

• The adaptive filters based on the LMS algorithm were chosen.

• Labview model and JAVA model were developed to test the ANC level & to obtain graphical views of controlled signal.

• The model will be developed in future for the defence applications.

• ?

Interior Noise Spectrum

Passive Techniques

– Sound Absorption

Sound Absorbing Material

Rigid

Wall

Reflected Sound Pressure (Pr)

Incident Sound Pressure (Pi)

2

1r

i

P

PNAC

Noise Absorption Coefficient

Back

Time Series Data of Input Noise Without Control

Time Series Data of Noise at Error Microphone With an Adaptive Controller Using a Step Size of 0.01 for the LMS Algorithm

Time Series Data of Noise at Error Microphone With an Adaptive Controller Using a Step

Size of 0.1 for the LMS Algorithm

Developed ANC Laboratory Experimental Model

REFERENCE

MICROPHONE

PRIMARY SOUND

SOURCE

ERROR

MICROPHONE

POWER AMPLIFIER

#2

POWER AMPLIFIER

#1

PRE AMPLIFIER #1

PRE AMPLIFIER #2 M6259 DATA ACQUISITION

CARD

PC

ENCLOSURE