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Digital Signal Processing in Audio and Acoustical Engineering [Kõva köide]

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(University of Salford), (University of Salford)
  • Formaat: Hardback, 242 pages, kõrgus x laius: 234x156 mm, kaal: 560 g, 175 Illustrations, black and white
  • Ilmumisaeg: 11-Apr-2019
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1466593881
  • ISBN-13: 9781466593886
Teised raamatud teemal:
Digital Signal Processing in Audio and Acoustical EngineeringSuurem pilt
  • Formaat: Hardback, 242 pages, kõrgus x laius: 234x156 mm, kaal: 560 g, 175 Illustrations, black and white
  • Ilmumisaeg: 11-Apr-2019
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1466593881
  • ISBN-13: 9781466593886
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781466593886.html
Märksõnad:
Starting with essential maths, fundamentals of signals and systems, and classical concepts of DSP, this book presents, from an application-oriented perspective, modern concepts and methods of DSP including machine learning for audio acoustics and engineering. Content highlights include but are not limited to room acoustic parameter measurements, filter design, codecs, machine learning for audio pattern recognition and machine audition, spatial audio, array technologies and hearing aids. Some research outcomes are fed into book as worked examples. As a research informed text, the book attempts to present DSP and machine learning from a new and more relevant angle to acousticians and audio engineers.

Some MATLAB® codes or frameworks of algorithms are given as downloads available on the CRC Press website. Suggested exploration and mini project ideas are given for "proof of concept" type of exercises and directions for further study and investigation. The book is intended for researchers, professionals, and senior year students in the field of audio acoustics.
Preface xi
About the Authors xiii
Chapter 1 Acoustic Signals and Audio Systems
1(20)
1.1 Signals and Systems
1(1)
1.2 Types of Systems by Properties
2(2)
1.3 Types of Signals
4(7)
1.3.1 Deterministic Signals
5(2)
1.3.2 Some Special Testing Signals
7(2)
1.3.3 Random Signals
9(2)
1.4 Statistics of Random Signals
11(5)
1.4.1 Probability Density Function and Moments
11(3)
1.4.2 Lag Statistical Analysis and Correlation Functions
14(1)
1.4.3 Gaussian Distribution and Central Limit Theorem
15(1)
1.5 Signals in Transformed Frequency Domains
16(5)
1.5.1 Fourier and Laplace Transforms
16(1)
1.5.2 Signal Statistics in the Frequency Domain
17(1)
1.5.3 Input-Output Relationships of LTI Systems
18(1)
Summary
19(1)
Bibliography and Extended Reading
20(1)
Exploration
20(1)
Chapter 2 Sampling Quantization and Discrete Fourier
21(22)
2.1 Sampling
21(4)
2.1.1 Time Discretization
22(1)
2.1.2 Aliasing
23(2)
2.2 Fourier
25(2)
2.3 Fourier Series of Periodic, Discrete-Time Signals
27(3)
2.4 Practical FFTs
30(6)
2.4.1 Positive and Negative Frequencies
30(1)
2.4.2 Windowing
31(1)
2.4.3 The Convolution Theorem
31(3)
2.4.4 Avoiding Spectral Smearing--More Windows
34(2)
2.5 Estimating Statistics Using Fourier Methods
36(3)
2.5.1 Cross Power Spectral Density Function
36(1)
2.5.2 Estimating the CPSD
37(2)
2.6 Transfer Function Measurement in Noise
39(4)
2.6.1 The Ordinary Coherence Function
39(2)
Summary
41(1)
Bibliography and Extended Reading
41(1)
Exploration
41(2)
Chapter 3 DSP in Acoustical Transfer Function Measurements
43(12)
3.1 Acoustical Transfer Function Measurement Problems
43(1)
3.2 Transfer Function Measurement Using MLS
44(5)
3.2.1 Maximum Length Sequences (MLSs)
45(3)
3.2.2 Some Useful Properties of MLS
48(1)
3.2.3 Measure Once
48(1)
3.2.4 No Truncation Errors
48(1)
3.2.5 Crest Factor
49(1)
3.3 Transfer Function Measurement Using Swept Sine Waves
49(6)
3.3.1 Matched Filtering
49(2)
Summary
51(1)
Bibliography and Extended Reading
52(1)
Exploration and Mini Project
52(3)
Chapter 4 Digital Filters and z-Transform
55(26)
4.1 General Introduction to Digital Filters
55(2)
4.2 Finite Impulse Response (FIR) Filters
57(2)
4.3 z-Transform and Transfer Function
59(1)
4.4 Zero-Pole Plots
60(4)
4.5 Infinite Impulse Response (IIR) Filters
64(1)
4.6 Stability
65(1)
4.7 Bilinear IIR Filters (BILINS)
66(2)
4.8 Biquadratic IIR Filter Design (Biquads)
68(2)
4.9 IIR Filter Design Using the Bilinear Transform
70(3)
4.9.1 Butterworth Low Pass Filters
71(2)
4.10 FIR Filter Design--The Fourier Transform Method
73(8)
4.10.1 Time/Frequency Effects
74(1)
4.10.2 Least Square Estimates of Transfer Functions
74(1)
4.10.3 Practical Filters Have Real Coefficients
74(1)
4.10.4 Zero Phase and Linear Phase Filters
75(2)
4.10.5 Recapitulation: FIR Filter Design Procedure
77(1)
Summary
77(1)
Bibliography and Extended Reading
78(1)
Exploration
78(3)
Chapter 5 Audio Codecs
81(18)
5.1 Audio Codecs
81(1)
5.2 Quantization and PCM family encoding
82(4)
5.2.1 Quantization as a Noise Source
82(1)
5.2.2 Quantization as a Distortion Process
83(1)
5.2.3 Dynamic Range due to Quantization
84(2)
5.3 Dither
86(1)
5.4 From PCM to DPCM
87(1)
5.5 Oversampling and Low Bit Converters
88(1)
5.6 One-Bit Conversion, Sigma-Delta Modulation
89(4)
5.7 Lossy Codecs and MPEG Codecs
93(6)
Summary
95(1)
References
95(1)
Bibliography and Extended Reading
96(1)
Exploration and Mini Project
96(3)
Chapter 6 DSP in Binaural Hearing and Microphone Arrays
99(16)
6.1 Head Related Transfer Function and Binaural Signal Processing
100(10)
6.1.1 Head Related Transfer Functions (HRTFs)
101(1)
6.1.2 HRTF Data
102(3)
6.1.3 Application Scenarios
105(5)
6.2 Microphone Arrays and Delay-Sum Beamformers
110(5)
Summary
113(1)
References
113(1)
Bibliography and Extended Reading
114(1)
Exploration
114(1)
Chapter 7 Adaptive Filters
115(26)
7.1 General Model of LMS Adaptive Filters
117(2)
7.2 Four Generic Types of Adaptive Filters
119(3)
7.2.1 System Identification
119(1)
7.2.2 Inverse Modelling
120(1)
7.2.3 Noise or Interference Cancellation
120(1)
7.2.4 Linear Prediction
121(1)
7.3 From Optimal Filter to Least Mean Square (LMS) Adaptive Algorithms
122(12)
7.3.1 Concept of Optimal Filters
122(5)
7.3.2 A Discrete-Time Formulation of Optimal Filter
127(1)
7.3.3 Adaptive Methods and LMS Algorithm
128(6)
7.4 Genetic Algorithms: Another Adaptive Technique
134(7)
7.4.1 Genetic Algorithms
135(3)
Summary
138(1)
Reference
139(1)
Bibliography and Extended Reading
139(1)
Exploration
139(2)
Chapter 8 Machine Learning in Acoustic DSP
141(46)
8.1 General Concept of Acoustic Pattern Recognition
141(1)
8.2 Common Acoustic Features
142(10)
8.2.1 Acoustic Features and Feature Spaces
142(1)
8.2.1.1 Time-Domain Features
143(2)
8.2.1.2 Frequency-Domain Features
145(4)
8.2.2 Time-Frequency Domain
149(1)
8.2.2.1 Mel-Frequency Cepstrum Coefficients
150(2)
8.3 Decision Making by Machine Learning
152(7)
8.3.1 Machine Learning
152(1)
8.3.2 Artificial Neural Network
152(1)
8.3.2.1 Neuron Models
153(2)
8.3.3 Topology of Artificial Neural Network
155(1)
8.3.4 Supervised Learning Rule
156(3)
8.4 Training, Testing and Validation
159(2)
8.4.1 Training and Testing
159(1)
8.4.1.1 Holdout Cross-Validation
160(1)
8.4.1.2 K-Fold Cross-Validation
160(1)
8.4.2 Over-Fitting and Under-Fitting
160(1)
8.4.3 Stop Criterion, Step Size, and Restart
161(1)
8.5 Speech Recognition
161(2)
8.6 Speaker Recognition
163(1)
8.7 Music Information Retrieval
163(1)
8.8 Machine Audition of Acoustics
164(14)
8.8.1 Acoustic Transmission Channels and Acoustic Parameters
165(4)
8.8.2 Extraction of Reverberation Time from Discrete Utterances
169(3)
8.8.3 Estimation of Speech Transmission Index from Running Speech
172(4)
8.8.4 Estimation of Reverberation Time from Running Speech
176(1)
8.8.5 Using Music as Stimuli
176(2)
8.9 Blind Estimation with a Parametric Model: Maximum Likelihood Estimation
178(9)
Summary
181(1)
References
181(2)
Bibliography and Extended Reading
183(1)
Recommended Software and Tool Kits
183(1)
Exploration and Mini Projects
184(3)
Chapter 9 Unsupervised Learning and Blind Source Separation
187(14)
9.1 Hebbian Learning (Self-Organised Learning)
188(1)
9.2 PCA Neural Networks
188(3)
9.2.1 Hebbian Maximum Eigenfilter and PCA
189(1)
9.2.2 Generalised Hebbian Algorithm and PCA Network
190(1)
9.3 ICA Neural Networks and Blind Source Separation
191(3)
9.4 Blind Estimation of Room Acoustic Parameters Using a PCA Network as a Feature Extractor
194(7)
Summary
197(1)
References
197(1)
Bibliography and Extended Reading
198(1)
Recommended Software and Tool Kits
198(1)
Exploration and Mini Project
198(3)
Chapter 10 DSP in Hearing Aids
201(20)
10.1 Technical Challenges of Hearing Aids
202(1)
10.2 Audiometry and Hearing Aid Fitting
202(4)
10.3 Filter Bank and Multi-Band Compression
206(7)
10.3.1 Filter Bank
206(4)
10.3.2 Compression Channel
210(3)
10.4 Acoustic Feedback Cancellation
213(3)
10.5 Transposition and Frequency Lowering
216(2)
10.6 Other Add-N Features
218(3)
Summary
218(1)
References
219(2)
Index 221
Appointed Senior Lecturer at Manchester Metropolitan University in 2001, before joining the University of Salford in 2006, Francis Li has accumulated a broad spectrum of expertise and research interests, including audio engineering, architectural and building acoustics, signal processing, artificial intelligence, soft-computing, data and voice communications, machine audition, speech technology, DSP applied to biomedical engineering, novel computer architecture, and control theory. He has published numerous research papers, reviewed for international journals and conferences, and acts as both Associate Editor-in-Chief for the CSC Journal Signal Processing, and Associate Technical Editor for the Journal of Audio Engineering Society.

Trevor J. Cox has been communicating acoustic engineering to the public for 15 years. Specialising in blind signal processing methods to model acoustics and assess human response to sounds, he has pioneered the concept of optimised diffusers and served as an acoustic expert for international standard organizations. He was a consultant for the worlds largest manufacturer of diffusing products RPG Diffusor Systems Inc. for over 10 years, and acts as convener of ISO Working Group WG25.