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" Audio source separation and speech enhancement / "
edited by Emmanuel Vincent, Tuomas Virtanen, Sharon Gannot.
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BL
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| Record Number
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840149
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| Title & Author
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Audio source separation and speech enhancement /\ edited by Emmanuel Vincent, Tuomas Virtanen, Sharon Gannot.
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| Publication Statement
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Hoboken, NJ :: John Wiley & Sons,, 2018.
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| Page. NO
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1 online resource
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| ISBN
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1119279860
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: 1119279887
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: 1119279917
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: 9781119279860
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: 9781119279884
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: 9781119279914
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9781119279891
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Table of Contents; List of Authors; Preface; Acknowledgment; Notations; Acronyms; About the Companion Website; Part I: Prerequisites; Chapter 1: Introduction; 1.1 Why are Source Separation and Speech Enhancement Needed?; 1.2 What are the Goals of Source Separation and Speech Enhancement?; 1.3 How can Source Separation and Speech Enhancement be Addressed?; 1.4 Outline; Bibliography; Chapter 2: Time-Frequency Processing: Spectral Properties; 2.1 Time-Frequency Analysis and Synthesis; 2.2 Source Properties in the Time-Frequency Domain; 2.3 Filtering in the Time-Frequency Domain
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2.4 SummaryBibliography; Chapter 3: Acoustics: Spatial Properties; 3.1 Formalization of the Mixing Process; 3.2 Microphone Recordings; 3.3 Artificial Mixtures; 3.4 Impulse Response Models; 3.5 Summary; Bibliography; Chapter 4: Multichannel Source Activity Detection, Localization, and Tracking; 4.1 Basic Notions in Multichannel Spatial Audio; 4.2 Multi-Microphone Source Activity Detection; 4.3 Source Localization; 4.4 Summary; Bibliography; Part II: Single-Channel Separation and Enhancement; Chapter 5: Spectral Masking and Filtering; 5.1 Time-Frequency Masking
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5.2 Mask Estimation Given the Signal Statistics5.3 Perceptual Improvements; 5.4 Summary; Bibliography; Chapter 6: Single-Channel Speech Presence Probability Estimation and Noise Tracking; 6.1 Speech Presence Probability and its Estimation; 6.2 Noise Power Spectrum Tracking; 6.3 Evaluation Measures; 6.4 Summary; Bibliography; Chapter 7: Single-Channel Classification and Clustering Approaches; 7.1 Source Separation by Computational Auditory Scene Analysis; 7.2 Source Separation by Factorial HMMs; 7.3 Separation Based Training; 7.4 Summary; Bibliography
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Chapter 10: Spatial Filtering10.1 Fundamentals of Array Processing; 10.2 Array Topologies; 10.3 Data-Independent Beamforming; 10.4 Data-Dependent Spatial Filters: Design Criteria; 10.5 Generalized Sidelobe Canceler Implementation; 10.6 Postfilters; 10.7 Summary; Bibliography; Chapter 11: Multichannel Parameter Estimation; 11.1 Multichannel Speech Presence Probability Estimators; 11.2 Covariance Matrix Estimators Exploiting SPP; 11.3 Methods for Weakly Guided and Strongly Guided RTF Estimation; 11.4 Summary; Bibliography; Chapter 12: Multichannel Clustering and Classification Approaches
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Chapter 8: Nonnegative Matrix Factorization8.1 NMF and Source Separation; 8.2 NMF Theory and Algorithms; 8.3 NMF Dictionary Learning Methods; 8.4 Advanced NMF Models; 8.5 Summary; Bibliography; Chapter 9: Temporal Extensions of Nonnegative Matrix Factorization; 9.1 Convolutive NMF; 9.2 Overview of Dynamical Models; 9.3 Smooth NMF; 9.4 Nonnegative State-Space Models; 9.5 Discrete Dynamical Models; 9.6 The Use of Dynamic Models in Source Separation; 9.7 Which Model to Use?; 9.8 Summary; 9.9 Standard Distributions; Bibliography; Part III: Multichannel Separation and Enhancement
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| Abstract
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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 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.
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| Subject
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Automatic speech recognition.
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Speech processing systems.
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Automatic speech recognition.
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COMPUTERS-- General.
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Speech processing systems.
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| Dewey Classification
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006.4/54
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| LC Classification
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TK7882.S65
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| Added Entry
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Gannot, Sharon
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Vincent, Emmanuel, (Research scientist)
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Virtanen, Tuomas
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