Digital Signal Processing in Audio and Acoustical Engineering 1st Edition by Francis F Li, Trevor J Cox ISBN 9781466593886 1466593881

Chapter 1 Acoustic Signals and Audio Systems

1.1 Signals and Systems

1.2 Types of Systems by Properties

1.3 Types of Signals

1.3.1 Deterministic Signals

1.3.2 Some Special Testing Signals

1.3.3 Random Signals

1.4 Statistics of Random Signals

1.4.1 Probability Density Function and Moments

1.4.2 Lag Statistical Analysis and Correlation Functions

1.4.3 Gaussian Distribution and Central Limit Theorem

1.5 Signals in Transformed Frequency Domains

1.5.1 Fourier and Laplace Transforms

1.5.2 Signal Statistics in the Frequency Domain

1.5.3 Input-Output Relationships of LTI Systems

Summary

Bibliography and Extended Reading

Exploration

Chapter 2 Sampling Quantization and Discrete Fourier

2.1 Sampling

2.1.1 Time Discretization

2.1.2 Aliasing

2.2 Fourier

2.3 Fourier Series of Periodic, Discrete-Time Signals

2.4 Practical FFTs

2.4.1 Positive and Negative Frequencies

2.4.2 Windowing

2.4.3 The Convolution Theorem

2.4.4 Avoiding Spectral Smearing—More Windows

2.5 Estimating Statistics Using Fourier Methods

2.5.1 Cross Power Spectral Density Function

2.5.2 Estimating the CPSD

2.6 Transfer Function Measurement in Noise

2.6.1 The Ordinary Coherence Function

Summary

Bibliography and Extended Reading

Exploration

Chapter 3 DSP in Acoustical Transfer Function Measurements

3.1 Acoustical Transfer Function Measurement Problems

3.2 Transfer Function Measurement Using MLS

3.2.1 Maximum Length Sequences (MLSs)

3.2.2 Some Useful Properties of MLS

3.2.3 Measure Once

3.2.4 No Truncation Errors

3.2.5 Crest Factor

3.3 Transfer Function Measurement Using Swept Sine Waves

3.3.1 Matched Filtering

Summary

Bibliography and Extended Reading

Exploration and Mini Project

Chapter 4 Digital Filters and z-Transform

4.1 General Introduction To Digital Filters

4.2 Finite Impulse Response (Fir) Filters

4.3 z-Transform and Transfer Function

4.4 Zero-Pole Plots

4.5 Infinite Impulse Response (IIR) Filters

4.6 Stability

4.7 Bilinear Iir Filters (BILINS)

4.8 Biquadratic Iir Filter Design (Biquads)

4.9 IIR Filter Design Using the Bilinear Transform

4.9.1 Butterworth Low Pass Filters

4.10 FIR Filter Design—The Fourier Transform Method

4.10.1 Time/Frequency Effects

4.10.2 Least Square Estimates of Transfer Functions

4.10.3 Practical Filters Have Real Coefficients

4.10.4 Zero Phase and Linear Phase Filters

4.10.5 Recapitulation: Fir Filter Design Procedure

Summary

Bibliography and Extended Reading

Exploration

Chapter 5 Audio Codecs

5.1 Audio Codecs

5.2 Quantization and PCM family encoding

5.2.1 Quantization as a Noise Source

5.2.2 Quantization as a Distortion Process

5.2.3 Dynamic Range due to Quantization

5.3 Dither

5.4 From PCM to DPCM

5.5 Oversampling and Low Bit Converters

5.6 One-Bit Conversion, Sigma-Delta Modulation

5.7 Lossy Codecs and MPEG Codecs

Summary

References

Bibliography and Extended Reading

Exploration and Mini Project

Chapter 6 DSP in Binaural Hearing and Microphone Arrays

6.1 Head Related Transfer Function and Binaural Signal Processing

6.1.1 Head Related Transfer Functions (HRTFs)

6.1.2 HRTF Data

6.1.3 Application Scenarios

6.2 Microphone Arrays and Delay-Sum Beamformers

Summary

References

Bibliography and Extended Reading

Exploration

Chapter 7 Adaptive Filters

7.1 General Model of LMS Adaptive Filters

7.2 Four Generic Types Of Adaptive Filters

7.2.1 System Identification

7.2.2 Inverse Modelling

7.2.3 Noise or Interference Cancellation

7.2.4 Linear Prediction

7.3 From Optimal Filter to Least Mean Square (LMS) Adaptive Algorithms

7.3.1 Concept of Optimal Filters

7.3.2 A Discrete-Time Formulation of Optimal Filter

7.3.3 Adaptive Methods and LMS Algorithm

7.4 Genetic Algorithms: Another Adaptive Technique

7.4.1 Genetic Algorithms

Summary

Reference

Bibliography and Extended Reading

Exploration

Chapter 8 Machine Learning in Acoustic DSP

8.1 General Concept of Acoustic Pattern Recognition

8.2 Common Acoustic Features

8.2.1 Acoustic Features and Feature Spaces

8.2.1.1 Time-Domain Features

8.2.1.2 Frequency-Domain Features

8.2.2 Time-Frequency Domain

8.2.2.1 Mel-Frequency Cepstrum Coefficients

8.3 Decision Making by Machine Learning

8.3.1 Machine Learning

8.3.2 Artificial Neural Network

8.3.2.1 Neuron Models

8.3.3 Topology of Artificial Neural Network

8.3.4 Supervised Learning Rule

8.4 Training, Testing and Validation

8.4.1 Training and Testing

8.4.1.1 Holdout Cross-Validation

8.4.1.2 K-Fold Cross-Validation

8.4.2 Over-Fitting and Under-Fitting

8.4.3 Stop Criterion, Step Size, and Restart

8.5 Speech Recognition

8.6 Speaker Recognition

8.7 Music Information Retrieval

8.8 Machine Audition of Acoustics

8.8.1 Acoustic Transmission Channels and Acoustic Parameters

8.8.2 Extraction of Reverberation Time from Discrete Utterances

8.8.3 Estimation of Speech Transmission Index from Running Speech

8.8.4 Estimation of Reverberation Time from Running Speech

8.8.5 Using Music as Stimuli

8.9 Blind Estimation with a Parametric Model: Maximum Likelihood Estimation

Summary

References

Bibliography and Extended Reading

Recommended Software and Tool Kits

Exploration and Mini Projects

Chapter 9 Unsupervised Learning and Blind Source Separation

9.1 Hebbian Learning (Self-Organised Learning)

9.2 PCA Neural Networks

9.2.1 Hebbian Maximum Eigenfilter and PCA

9.2.2 Generalised Hebbian Algorithm and PCA Network

9.3 ICA Neural Networks and Blind Source Separation

9.4 Blind Estimation of Room Acoustic Parameters Using a PCA Network as a Feature Extractor

Summary

References

Bibliography and Extended Reading

Recommended Software and Tool Kits

Exploration and Mini Project

Chapter 10 DSP in Hearing Aids

10.1 Technical Challenges of Hearing Aids

10.2 Audiometry and Hearing Aid Fitting

10.3 Filter Bank and Multi-Band Compression

10.3.1 Filter Bank

10.3.2 Compression Channel

10.4 Acoustic Feedback Cancellation

10.5 Transposition and Frequency Lowering

10.6 Other Add-N Features

Summary

References

Index