Multimedia signal processing : theory and applications in speech, music and communications

Title:

Personal Author:

Vaseghi, Saeed V.

Publication Information:

Chichester, West Sussex : John Wiley & Sons, 2007

ISBN:

9780470062012

Subject Term:

Signal processing

Multimedia systems

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010160581	TK5102.9 V374 2007	Open Access Book	Book	Searching... Unknown

Multimedia Signal Processing is a comprehensive and accessible text to the theory and applications of digital signal processing (DSP). The applications of DSP are pervasive and include multimedia systems, cellular communication, adaptive network management, radar, pattern recognition, medical signal processing, financial data forecasting, artificial intelligence, decision making, control systems and search engines.

This book is organised in to three major parts making it a coherent and structured presentation of the theory and applications of digital signal processing. A range of important topics are covered in basic signal processing, model-based statistical signal processing and their applications.

Part 1: Basic Digital Signal Processing gives an introduction to the topic, discussing sampling and quantization, Fourier analysis and synthesis, Z-transform, and digital filters.

Part 2: Model-based Signal Processing covers probability and information models, Bayesian inference, Wiener filter, adaptive filters, linear prediction hidden Markov models and independent component analysis.

Part 3: Applications of Signal Processing in Speech, Music and Telecommunications explains the topics of speech and music processing, echo cancellation, deconvolution and channel equalization, and mobile communication signal processing.

Covers music signal processing, explains the anatomy and psychoacoustics of hearing and the design of MP3 music coder Examines speech processing technology including speech models, speech coding for mobile phones and speech recognition Covers single-input and multiple-inputs denoising methods, bandwidth extension and the recovery of lost speech packets in applications such as voice over IP (VoIP) Illustrated throughout, including numerous solved problems, Matlab experiments and demonstrations Companion website features Matlab and C++ programs with electronic copies of all figures.

This book is ideal for researchers, postgraduates and senior undergraduates in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be a valuable text to professional engineers in telecommunications and audio and signal processing industries.

Author Notes

Saeed Vaseghi is Professor of Communications and Signal Processing at Brunel University's Department of Electronics and Computer Engineering and is Group Leader for the Communications & Multimedia Signal Processing Group. Previously, Saeed obtained a first in Electrical and Electronics Engineering from Newcastle University, and a PhD in Digital Signal Processing from Cambridge University.
His PhD in noisy signal restoration led to establishment of CEDAR, the world's leading system for restoration of audio signals. Saeed also held a British Telecom lectureship at UEA Norwich, and a readership at Queen's University of Belfast before his move to Brunel.

Preface	p. xiii
Acknowledgement	p. xvii
Symbols	p. xix
Abbreviations	p. xxiii
Part I Basic Digital Signal Processing	p. 1
1 Introduction	p. 3
1.1 Signals and Information	p. 3
1.2 Signal Processing Methods	p. 5
1.3 Applications of Digital Signal Processing	p. 8
1.4 Summary	p. 23
2 Fourier Analysis and Synthesis	p. 25
2.1 Introduction	p. 25
2.2 Fourier Series: Representation of Periodic Signals	p. 27
2.3 Fourier Transform: Representation of Nonperiodic Signals	p. 33
2.4 Discrete Fourier Transform	p. 48
2.5 Short-Time Fourier Transform	p. 57
2.6 Fast Fourier Transform (FFT)	p. 59
2.7 2-D Discrete Fourier Transform (2-D DFT)	p. 65
2.8 Discrete Cosine Transform (DCT)	p. 66
2.9 Some Applications of the Fourier Transform	p. 68
2.10 Summary	p. 74
3 z-Transform	p. 79
3.1 Introduction	p. 79
3.2 Derivation of the z-Transform	p. 81
3.3 The z-Plane and the Unit Circle	p. 83
3.4 Properties of z-Transform	p. 88
3.5 z-Transfer Function, Poles (Resonance) and Zeros (Anti-resonance)	p. 91
3.6 z-Transform of Analysis of Exponential Transient Signals	p. 100
3.7 Inverse z-Transform	p. 104
3.8 Summary	p. 106
4 Digital Filters	p. 111
4.1 Introduction	p. 111
4.2 Linear Time-Invariant Digital Filters	p. 113
4.3 Recursive and Non-Recursive Filters	p. 115
4.4 Filtering Operation: Sum of Vector Products, A Comparison of Convolution and Correlation	p. 117
4.5 Filter Structures: Direct, Cascade and Parallel Forms	p. 119
4.6 Linear Phase FIR Filters	p. 122
4.7 Design of Digital FIR Filter-banks	p. 136
4.8 Quadrature Mirror Sub-band Filters	p. 139
4.9 Design of Infinite Impulse Response (IIR) Filters by Pole-zero Placements	p. 145
4.10 Issues in the Design and Implementation of a Digital Filter	p. 148
4.11 Summary	p. 148
5 Sampling and Quantisation	p. 155
5.1 Introduction	p. 155
5.2 Sampling a Continuous-Time Signal	p. 158
5.3 Quantisation	p. 162
5.4 Sampling Rate Conversion: Interpolation and Decimation	p. 166
5.5 Summary	p. 171
Part II Model-Based Signal Processing	p. 173
6 Information Theory and Probability Models	p. 175
6.1 Introduction: Probability and Information Models	p. 176
6.2 Random Processes	p. 177
6.3 Probability Models of Random Signals	p. 182
6.4 Information Models	p. 189
6.5 Stationary and Non-Stationary Random Processes	p. 199
6.6 Statistics (Expected Values) of a Random Process	p. 202
6.7 Some Useful Practical Classes of Random Processes	p. 212
6.8 Transformation of a Random Process	p. 225
6.9 Search Engines: Citation Ranking	p. 230
6.10 Summary	p. 231
7 Bayesian Inference	p. 233
7.1 Bayesian Estimation Theory: Basic Definitions	p. 233
7.2 Bayesian Estimation	p. 242
7.3 Expectation Maximisation Method	p. 255
7.4 Cramer-Rao Bound on the Minimum Estimator Variance	p. 257
7.5 Design of Gaussian Mixture Models (GMM)	p. 260
7.6 Bayesian Classification	p. 263
7.7 Modelling the Space of a Random Process	p. 270
7.8 Summary	p. 273
8 Least Square Error, Wiener-Kolmogorov Filters	p. 275
8.1 Least Square Error Estimation: Wiener-Kolmogorov Filter	p. 275
8.2 Block-Data Formulation of the Wiener Filter	p. 280
8.3 Interpretation of Wiener Filter as Projection in Vector Space	p. 282
8.4 Analysis of the Least Mean Square Error Signal	p. 284
8.5 Formulation of Wiener Filters in the Frequency Domain	p. 285
8.6 Some Applications of Wiener Filters	p. 286
8.7 Implementation of Wiener Filters	p. 292
8.8 Summary	p. 294
9 Adaptive Filters: Kalman, RLS, LMS	p. 297
9.1 Introduction	p. 297
9.2 State-Space Kalman Filters	p. 299
9.3 Sample Adaptive Filters	p. 307
9.4 Recursive Least Square (RLS) Adaptive Filters	p. 309
9.5 The Steepest-Descent Method	p. 313
9.6 LMS Filter	p. 317
9.7 Summary	p. 321
10 Linear Prediction Models	p. 323
10.1 Linear Prediction Coding	p. 323
10.2 Forward, Backward and Lattice Predictors	p. 332
10.3 Short-Term and Long-Term Predictors	p. 339
10.4 MAP Estimation of Predictor Coefficients	p. 341
10.5 Formant-Tracking LP Models	p. 343
10.6 Sub-Band Linear Prediction Model	p. 344
10.7 Signal Restoration Using Linear Prediction Models	p. 345
10.8 Summary	p. 350
11 Hidden Markov Models	p. 353
11.1 Statistical Models for Non-Stationary Processes	p. 353
11.2 Hidden Markov Models	p. 355
11.3 Training Hidden Markov Models	p. 361
11.4 Decoding Signals Using Hidden Markov Models	p. 367
11.5 HMM in DNA and Protein Sequences	p. 371
11.6 HMMs for Modelling Speech and Noise	p. 372
11.7 Summary	p. 378
12 Eigenvector Analysis, Principal Component Analysis and Independent Component Analysis	p. 381
12.1 Introduction - Linear Systems and Eigenanalysis	p. 382
12.2 Eigenvectors and Eigenvalues	p. 386
12.3 Principal Component Analysis (PCA)	p. 389
12.4 Independent Component Analysis	p. 393
12.5 Summary	p. 412
Part III Applications of Digital Signal Processing to Speech, Music and Telecommunications	p. 415
13 Music Signal Processing and Auditory Perception	p. 417
13.1 Introduction	p. 418
13.2 Musical Notes, Intervals and Scales	p. 418
13.3 Musical Instruments	p. 426
13.4 Review of Basic Physics of Sounds	p. 439
13.5 Music Signal Features and Models	p. 447
13.6 Anatomy of the Ear and the Hearing Process	p. 451
13.7 Psychoacoustics of Hearing	p. 462
13.8 Music Coding (Compression)	p. 471
13.9 High Quality Audio Coding: MPEG Audio Layer-3 (MP3)	p. 475
13.10 Stereo Music Coding	p. 478
13.11 Summary	p. 480
14 Speech Processing	p. 483
14.1 Speech Communication	p. 483
14.2 Acoustic Theory of Speech: The Source-filter Model	p. 484
14.3 Speech Models and Features	p. 490
14.4 Linear Prediction Models of Speech	p. 491
14.5 Harmonic Plus Noise Model of Speech	p. 492
14.6 Fundamental Frequency (Pitch) Information	p. 496
14.7 Speech Coding	p. 500
14.8 Speech Recognition	p. 510
14.9 Summary	p. 525
15 Speech Enhancement	p. 527
15.1 Introduction	p. 528
15.2 Single-Input Speech Enhancement Methods	p. 528
15.3 Speech Bandwidth Extension - Spectral Extrapolation	p. 547
15.4 Interpolation of Lost Speech Segments - Packet Loss Concealment	p. 553
15.5 Multi-Input Speech Enhancement Methods	p. 562
15.6 Speech Distortion Measurements	p. 565
15.7 Summary	p. 569
16 Echo Cancellation	p. 573
16.1 Introduction: Acoustic and Hybrid Echo	p. 573
16.2 Telephone Line Hybrid Echo	p. 575
16.3 Hybrid (Telephone Line) Echo Suppression	p. 577
16.4 Adaptive Echo Cancellation	p. 578
16.5 Acoustic Echo	p. 581
16.6 Sub-Band Acoustic Echo Cancellation	p. 584
16.7 Echo Cancellation with Linear Prediction Pre-whitening	p. 585
16.8 Multi-Input Multi-Output Echo Cancellation	p. 586
16.9 Summary	p. 589
17 Channel Equalisation and Blind Deconvolution	p. 591
17.1 Introduction	p. 591
17.2 Blind Equalisation Using Channel Input Power Spectrum	p. 598
17.3 Equalisation Based on Linear Prediction Models	p. 601
17.4 Bayesian Blind Deconvolution and Equalisation	p. 603
17.5 Blind Equalisation for Digital Communication Channels	p. 611
17.6 Equalisation Based on Higher-Order Statistics	p. 616
17.7 Summary	p. 623
18 Signal Processing in Mobile Communication	p. 625
18.1 Introduction to Cellular Communication	p. 625
18.2 Communication Signal Processing in Mobile Systems	p. 631
18.3 Capacity, Noise, and Spectral Efficiency	p. 632
18.4 Multi-path and Fading in Mobile Communication	p. 634
18.5 Smart Antennas - Space-Time Signal Processing	p. 639
18.6 Summary	p. 642
Index	p. 643

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