Audio Source Separation and Speech Enhancement (Hardcover)

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 Pertilä, 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
Cédric Févotte, Paris Smaragdis, NasserMohammadiha, and Gautham J.Mysore

9.1 Convolutive NMF 161

9.2 Overview of DynamicalModels 169

9.3 Smooth NMF 170

9.4 Nonnegative State-Space Models 174

9.5 Discrete DynamicalModels 178

9.6 The Use of DynamicModels in Source Separation 182

9.7 Which Model to Use? 183

9.8 Summary 184

9.9 Standard Distributions 184

Bibliography 185

Part III Multichannel Separation and Enhancement 189

10 Spatial Filtering 191
Shmulik Markovich-Golan,Walter Kellermann, and Sharon Gannot

10.1 Fundamentals of Array Processing 192

10.2 Array Topologies 197

10.3 Data-Independent Beamforming 199

10.4 Data-Dependent Spatial Filters: Design Criteria 202

10.5 Generalized Sidelobe Canceler Implementation 209

10.6 Postfilters 210

10.7 Summary 211

Bibliography 212

11 Multichannel Parameter Estimation 219
Shmulik Markovich-Golan,Walter Kellermann, and Sharon Gannot

11.1 Multichannel Speech Presence Probability Estimators 219

11.2 Covariance Matrix Estimators Exploiting SPP 227

11.3 Methods forWeakly Guided and Strongly Guided RTF Estimation 228

11.4 Summary 231

Bibliography 231

12 Multichannel Clustering and Classification Approaches 235
Michael I.Mandel, Shoko Araki, and Tomohiro Nakatani

12.1 Two-Channel Clustering 236

12.2 Multichannel Clustering 244

12.3 Multichannel Classification 251

12.4 Spatial Filtering Based on Masks 255

12.5 Summary 257

Bibliography 258

13 Independent Component and Vector Analysis 263
Hiroshi Sawada and Zbynˇek Koldovský

13.1 Convolutive Mixtures and their Time-Frequency Representations 264

13.2 Frequency-Domain Independent Component Analysis 265

13.3 Independent Vector Analysis 279

13.4 Example 280

13.5 Summary 284

Bibliography 284

14 Gaussian Model Based Multichannel Separation 289
Alexey Ozerov and Hirokazu Kameoka

14.1 Gaussian Modeling 289

14.2 Library of Spectral and SpatialModels 295

14.3 Parameter Estimation Criteria and Algorithms 300

14.4 Detailed Presentation of Some Methods 305

14.5 Summary 312

Acknowledgment 312

Bibliography 312

15 Dereverberation 317
Emanuël A.P. Habets and Patrick A. Naylor

15.1 Introduction to Dereverberation 317

15.2 Reverberation Cancellation Approaches 319

15.3 Reverberation Suppression Approaches 329

15.4 Direct Estimation 335

15.5 Evaluation of Dereverberation 336

15.6 Summary 337

Bibliography 337

Part IV Application Scenarios and Perspectives 345

16 Applying Source Separation to Music 347
Bryan Pardo, Antoine Liutkus, Zhiyao Duan, and Gaël Richard

16.1 Challenges and Opportunities 348

16.2 Nonnegative Matrix Factorization in the Case of Music 349

16.3 Taking Advantage of the Harmonic Structure of Music 354

16.4 Nonparametric Local Models: Taking Advantage of Redundancies in Music 358

16.5 Taking Advantage of Multiple Instances 363

16.6 Interactive Source Separation 367

16.7 Crowd-Based Evaluation 367

16.8 Some Examples of Applications 368

16.9 Summary 370

Bibliography 370

17 Application of Source Separation to Robust Speech Analysis and Recognition 377
ShinjiWatanabe, Tuomas Virtanen, and Dorothea Kolossa

17.1 Challenges and Opportunities 377

17.2 Applications 380

17.3 Robust Speech Analysis and Recognition 390

17.4 Integration of Front-End and Back-End 397

17.5 Use of Multimodal Information with Source Separation 403

17.6 Summary 404

Bibliography 405

18 Binaural Speech Processing with Application to Hearing Devices 413
Simon Doclo, Sharon Gannot, Daniel Marquardt, and Elior Hadad

18.1 Introduction to Binaural Processing 413

18.2 Binaural Hearing 415

18.3 Binaural Noise Reduction Paradigms 416

18.4 The Binaural Noise Reduction Problem 420

18.5 Extensions for Diffuse Noise 425

18.6 Extensions for Interfering Sources 431

18.7 Summary 437

Bibliography 437

19 Perspectives 443
Emmanuel Vincent, Tuomas Virtanen, and Sharon Gannot

19.1 Advancing Deep Learning 443

19.2 Exploiting Phase Relationships 447

19.3 AdvancingMultichannel Processing 450

19.4 Addressing Multiple-Device Scenarios 453

19.5 TowardsWidespread Commercial Use 455

Acknowledgment 457

Bibliography 457

Index 465