Book description
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.
Table of contents
- Cover
- List of Authors
- Preface
- Acknowledgment
- Notations
- Acronyms
- About the Companion Website
- Part I: Prerequisites
-
Part II: Single‐Channel Separation and Enhancement
- Chapter 5: Spectral Masking and Filtering
- Chapter 6: Single‐Channel Speech Presence Probability Estimation and Noise Tracking
- Chapter 7: Single‐Channel Classification and Clustering Approaches
- Chapter 8: Nonnegative Matrix Factorization
- Chapter 9: Temporal Extensions of Nonnegative Matrix Factorization
- Part III: Multichannel Separation and Enhancement
-
Part IV: Application Scenarios and Perspectives
-
Chapter 16: Applying Source Separation to Music
- 16.1 Challenges and Opportunities
- 16.2 Nonnegative Matrix Factorization in the Case of Music
- 16.3 Taking Advantage of the Harmonic Structure of Music
- 16.4 Nonparametric Local Models: Taking Advantage of Redundancies in Music
- 16.5 Taking Advantage of Multiple Instances
- 16.6 Interactive Source Separation
- 16.7 Crowd‐Based Evaluation
- 16.8 Some Examples of Applications
- 16.9 Summary
- Bibliography
- Chapter 17: Application of Source Separation to Robust Speech Analysis and Recognition
- Chapter 18: Binaural Speech Processing with Application to Hearing Devices
- Chapter 19: Perspectives
-
Chapter 16: Applying Source Separation to Music
- Index
- End User License Agreement
Product information
- Title: Audio Source Separation and Speech Enhancement
- Author(s):
- Release date: October 2018
- Publisher(s): Wiley
- ISBN: 9781119279891
You might also like
book
Communication Acoustics: An Introduction to Speech, Audio and Psychoacoustics
In communication acoustics, the communication channel consists of a sound source, a channel (acoustic and/or electric) …
book
Handbook of Blind Source Separation
Edited by the people who were forerunners in creating the field, together with contributions from 34 …
book
Intelligent Speech Signal Processing
Intelligent Speech Signal Processing investigates the utilization of speech analytics across several systems and real-world activities, …
book
Techniques for Noise Robustness in Automatic Speech Recognition
Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace …