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INTRODUCTION TO 18-792 ADVANCED DIGITAL SIGNAL PROCESSING Richard M. Stern 18-792 lecture August 25, 2014 Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh, Pennsylvania 15213

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Carnegie Mellon Slide 2 18-792 Advanced Digital Signal Processing Welcome to 18-792 Advanced DSP! Today will Review mechanics of course Review course content Preview material in 18-792 (Advanced DSP)

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Carnegie Mellon Slide 3 18-792 Advanced Digital Signal Processing Important people (for this course at least) Instructor: Richard Stern PH B24, 8-2535, rms@cs.cmu.edu Teaching intern: Anjali Menon PH B43, anjalim@andrew.cmu.edu Course management assistant: Chelsea Mastilak HH 1112, 8-4951, cmastilak@andrew.cmu.edu

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Carnegie Mellon Slide 4 18-792 Advanced Digital Signal Processing Some course details Meeting time and place: here and now; recitations Friday 10:30 12:20, PH 226B Pre-requisites (you really need these!): Basic DSP course like 18-491 Basic probability course like 36-217 Some MATLAB or C background (MATLAB most useful) (Stochastic processes not needed) Grades based on: Machine problems and other homework (40-50%) Three exams (50-60%) Two midterms (October 8 and November 19), and final exam

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Carnegie Mellon Slide 5 18-792 Advanced Digital Signal Processing Textbooks Major texts: Lim and Oppenheim: Advanced Topics in Signal Processing (out of print) Oppenheim and Schafer: Discrete-Time Signal Processing (from last semester) Material to be supplemented by papers and other sources Many other texts listed

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Carnegie Mellon Slide 6 18-792 Advanced Digital Signal Processing Other support sources Office hours: Two hours per week for both Stern and Menon, times TBA You can schedule additional times with me as needed Course home page: http://www.ece.cmu.edu/~ece792http://www.ece.cmu.edu/~ece792 Blackboard to be used for grades (but basically nothing else)

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Carnegie Mellon Slide 7 18-792 Advanced Digital Signal Processing Academic integrity (i.e. cheating and plagiarism) CMUs take on academic integrity: http://www.cmu.edu/policies/documents/Cheating.htmlhttp://www.cmu.edu/policies/documents/Cheating.html Most important rule: Dont cheat! But what do we mean by that? Discussing general strategies on homework with other students is OK Solving homework together is NOT OK Accessing material from previous years is NOT OK Collaborating on exams is REALLY REALLY NOT OK!

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Carnegie Mellon Slide 8 18-792 Advanced Digital Signal Processing 18-792: major topic areas Overview of important properties of stochastic processes Traditional and modern spectral analysis Linear prediction Multi-rate DSP Short-time Fourier analysis Adaptive filtering Adaptive array processing Additional topics and applications

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Carnegie Mellon Slide 9 18-792 Advanced Digital Signal Processing Introduction to random processes Stochastic process definitions and properties Ensemble and time averages Power spectral density functions and their computation Random processes and linear filters Gaussian and other special random processes

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Carnegie Mellon Slide 10 18-792 Advanced Digital Signal Processing Traditional and modern spectral analysis Introduction to statistical estimation and estimators Estimates of autocorrelation functions Traditional approaches based on the periodogram Performance of smoothed spectral estimates Nonlinear estimation: the maximum entropy method Parametric approaches to spectral estimation; linear prediction

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Carnegie Mellon Slide 11 18-792 Advanced Digital Signal Processing Linear prediction Linear prediction using covariance and autocorrelation approaches Levinson-Durbin recursion and Cholesky decomposition Design and interpretation of lattice filters Applications to speech, bioinformation processing, and geophysics

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Carnegie Mellon Slide 12 18-792 Advanced Digital Signal Processing Multi-rate digital signal processing Review of sampling rate conversion Polyphase implementation of FIR filters for rate conversion Multistage implementations, with application to speech and music analysis

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Carnegie Mellon Slide 13 18-792 Advanced Digital Signal Processing Short-time Fourier analysis Interpretation as windowed Fourier transform or filter bank Filter design techniques Analysis-synthesis systems Applications to speech and music analysis Phase vocoding Manipulation of time and frequency Generalized time-frequency representations Wigner distributions and wavelet functions

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Carnegie Mellon Slide 14 18-792 Advanced Digital Signal Processing Adaptive filtering Introduction to adaptive signal processing Objective measures of goodness Least squares derivations Steepest descent The LMS and RLS algorithms Adaptive lattice filters Kalman filters Multi-sensor adaptive array processing and beamforming

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Carnegie Mellon Slide 15 18-792 Advanced Digital Signal Processing Some possible additional topics Homomorphic signal processing and the complex cepstrum Blind source separation Signal processing for speech analysis, synthesis, and recognition

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Carnegie Mellon Slide 16 18-792 Advanced Digital Signal Processing Advanced digital signal processing: major application issues Signal representation Signal modeling Signal enhancement Signal separation

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Carnegie Mellon Slide 17 18-792 Advanced Digital Signal Processing Signal representation: why perform signal processing? A look at the time-domain waveform of six: Its hard to infer much from the time-domain waveform

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Carnegie Mellon Slide 18 18-792 Advanced Digital Signal Processing Signal representation: why perform signal processing? A speech waveform in time: Welcome to DSP I

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Carnegie Mellon Slide 19 18-792 Advanced Digital Signal Processing A time-frequency representation of welcome is much more informative

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Carnegie Mellon Slide 20 18-792 Advanced Digital Signal Processing Signal modeling: lets consider the uh in welcome:

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Carnegie Mellon Slide 21 18-792 Advanced Digital Signal Processing The raw spectrum

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Carnegie Mellon Slide 22 18-792 Advanced Digital Signal Processing All-pole modeling: the LPC spectrum

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Carnegie Mellon Slide 23 18-792 Advanced Digital Signal Processing The source-filter model of speech A useful model for representing the generation of speech sounds: Pitch Pulse train source Noise source Vocal tract model Amplitude p[n]

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Carnegie Mellon Slide 24 18-792 Advanced Digital Signal Processing An application of LPC modeling: separating the vocal tract excitation and and filter Original speech: Speech with 75-Hz excitation: Speech with 150 Hz excitation: Speech with noise excitation: Comment: this is a major techniques used in speech coding

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Carnegie Mellon Slide 25 18-792 Advanced Digital Signal Processing Approach of Acero, Liu, Moreno, et al. (1990-1997) Compensation achieved by estimating parameters of noise and filter and applying inverse operations Clean speech x[m] h[m] n[m] z[m] Linear filtering Degraded speech Additive noise Classical signal enhancement: compensation of speech for noise and filtering

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Carnegie Mellon Slide 26 18-792 Advanced Digital Signal Processing Classical combined compensation improves accuracy in stationary environments Threshold shifts by ~7 dB Accuracy still poor for low SNRs CMN (baseline) Complete retraining VTS (1997) CDCN (1990) 7 dB 13 dB Clean Original Recovered

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Carnegie Mellon Slide 27 18-792 Advanced Digital Signal Processing Another type of signal enhancement: adaptive noise cancellation Speech + noise enters primary channel, correlated noise enters reference channel Adaptive filter attempts to convert noise in secondary channel to best resemble noise in primary channel and subtracts Performance degrades when speech leaks into reference channel and in reverberation

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Carnegie Mellon Slide 28 18-792 Advanced Digital Signal Processing Simulation of noise cancellation for a PDA using two mics in endfire configuration Speech in cafeteria noise, no noise cancellation Speech with noise cancellation But . simulation assumed no reverb

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Carnegie Mellon Slide 29 18-792 Advanced Digital Signal Processing Signal separation: speech is quite intelligible, even when presented only in fragments Procedure: Determine which time-frequency time-frequency components appear to be dominated by the desired signal Reconstruct signal based on good components A Monaural example: Mixed signals - Separated signals -

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Carnegie Mellon Slide 30 18-792 Advanced Digital Signal Processing Practical signal separation: Audio samples using selective reconstruction based on ITD RT60 (ms) 0 300 No Proc Delay-sum ZCAE-bin ZCAE-cont

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Carnegie Mellon Slide 31 18-792 Advanced Digital Signal Processing Summary Lots of interesting topics that extend core material from DSP Greater emphasis on implementation and applications Greater emphasis on statistically-optimal signal processing I hope that you have as much fun with this material as I have had!

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