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Audio Source Separation and Speech Enhancement

Audio Source Separation and Speech Enhancement

Wydawnictwo Blackwell Science
Data wydania 01/10/2018
Wydanie Pierwsze
Liczba stron 504
Forma publikacji książka w twardej oprawie
Poziom zaawansowania Dla szkół wyższych i kształcenia podyplomowego
Język angielski
ISBN 9781119279891
Kategorie Audiologia i otologia, Inżynieria komunikacyjna i telekomunikacyjna
565.00 PLN (z VAT)
$142.60 / €132.04 / £110.54 /
Produkt dostępny
Dostawa 2 dni
Do schowka

Opis książki

Learn the technology behind hearing aids, Siri, and Echo Audio source separation and speech enhancement aim to extract one or more source signals of interest from an audio recording involving several sound sources. These technologies are among the most studied in audio signal processing today and bear a critical role in the success of hearing aids, hands-free phones, voice command and other noise-robust audio analysis systems, and music post-production software. Research on this topic has followed three convergent paths, starting with sensor array processing, computational auditory scene analysis, and machine learning based approaches such as independent component analysis, respectively. This book is the first one to provide a comprehensive overview by presenting the common foundations and the differences between these techniques in a unified setting. Key features: * Consolidated perspective on audio source separation and speech enhancement. * Both historical perspective and latest advances in the field, e.g. deep neural networks. * Diverse disciplines: array processing, machine learning, and statistical signal processing. * Covers the most important techniques for both single-channel and multichannel processing. This book provides both introductory and advanced material suitable for people with basic knowledge of signal processing and machine learning. Thanks to its comprehensiveness, it will help students select a promising research track, researchers leverage the acquired cross-domain knowledge to design improved techniques, and engineers and developers choose the right technology for their target application scenario. It will also be useful for practitioners from other fields (e.g., acoustics, multimedia, phonetics, and musicology) willing to exploit audio source separation or speech enhancement as pre-processing tools for their own needs.

Audio Source Separation and Speech Enhancement

Spis treści

List of Authors xvii

Preface xxi

Acknowledgment xxiii

Notations xxv

Acronyms xxix

About the Companion Website xxxi

Part I Prerequisites 1

1 Introduction 3
Emmanuel Vincent, Sharon Gannot, and Tuomas Virtanen

1.1 Why are Source Separation and Speech Enhancement Needed? 3

1.2 What are the Goals of Source Separation and Speech Enhancement? 4

1.3 How can Source Separation and Speech Enhancement be Addressed? 9

1.4 Outline 11

Bibliography 12

2 Time-Frequency Processing: Spectral Properties 15
Tuomas Virtanen, Emmanuel Vincent, and Sharon Gannot

2.1 Time-Frequency Analysis and Synthesis 15

2.2 Source Properties in the Time-Frequency Domain 23

2.3 Filtering in the Time-Frequency Domain 25

2.4 Summary 28

Bibliography 28

3 Acoustics: Spatial Properties 31
Emmanuel Vincent, Sharon Gannot, and Tuomas Virtanen

3.1 Formalization of the Mixing Process 31

3.2 Microphone Recordings 32

3.3 Artificial Mixtures 36

3.4 Impulse Response Models 37

3.5 Summary 43

Bibliography 43

4 Multichannel Source Activity Detection, Localization, and Tracking 47
Pasi Pertila, Alessio Brutti, Piergiorgio Svaizer, and Maurizio Omologo

4.1 Basic Notions in Multichannel Spatial Audio 47

4.2 Multi-Microphone Source Activity Detection 52

4.3 Source Localization 54

4.4 Summary 60

Bibliography 60

Part II Single-Channel Separation and Enhancement 65

5 Spectral Masking and Filtering 67
Timo Gerkmann and Emmanuel Vincent

5.1 Time-Frequency Masking 67

5.2 Mask Estimation Given the Signal Statistics 70

5.3 Perceptual Improvements 81

5.4 Summary 82

Bibliography 83

6 Single-Channel Speech Presence Probability Estimation and Noise Tracking 87
Rainer Martin and Israel Cohen

6.1 Speech Presence Probability and its Estimation 87

6.2 Noise Power Spectrum Tracking 93

6.3 Evaluation Measures 102

6.4 Summary 104

Bibliography 104

7 Single-Channel Classification and Clustering Approaches 107
FelixWeninger, Jun Du, Erik Marchi, and Tian Gao

7.1 Source Separation by Computational Auditory Scene Analysis 108

7.2 Source Separation by Factorial HMMs 111

7.3 Separation Based Training 113

7.4 Summary 125

Bibliography 125

8 Nonnegative Matrix Factorization 131
Roland Badeau and Tuomas Virtanen

8.1 NMF and Source Separation 131

8.2 NMF Theory and Algorithms 137

8.3 NMF Dictionary LearningMethods 145

8.4 Advanced NMF Models 148

8.5 Summary 156

Bibliography 156

9 Temporal Extensions of Nonnegative Matrix Factorization 161
Cedric Fevotte, Paris Smaragdis, NasserMohammadiha

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