Audio Source Separation and Speech Enhancement (eBook, PDF)
Redaktion: Vincent, Emmanuel; Gannot, Sharon; Virtanen, Tuomas
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Audio Source Separation and Speech Enhancement (eBook, PDF)
Redaktion: Vincent, Emmanuel; Gannot, Sharon; Virtanen, Tuomas
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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…mehr
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- Produktdetails
- Verlag: John Wiley & Sons
- Seitenzahl: 504
- Erscheinungstermin: 24. Juli 2018
- Englisch
- ISBN-13: 9781119279884
- Artikelnr.: 53464262
- Verlag: John Wiley & Sons
- Seitenzahl: 504
- Erscheinungstermin: 24. Juli 2018
- Englisch
- ISBN-13: 9781119279884
- Artikelnr.: 53464262
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 ZbynÇek
Koldovsk? 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
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 ZbynÇek
Koldovsk? 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