Speech Enhancement
A Signal Subspace Perspective
- 1st Edition - January 4, 2014
- Authors: Jacob Benesty, Jesper Rindom Jensen, Mads Graesboll Christensen, Jingdong Chen
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 0 0 1 3 9 - 4
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 0 0 2 5 3 - 7
Speech enhancement is a classical problem in signal processing, yet still largely unsolved. Two of the conventional approaches for solving this problem are linear filtering, li… Read more
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Request a sales quoteSpeech enhancement is a classical problem in signal processing, yet still largely unsolved. Two of the conventional approaches for solving this problem are linear filtering, like the classical Wiener filter, and subspace methods. These approaches have traditionally been treated as different classes of methods and have been introduced in somewhat different contexts. Linear filtering methods originate in stochastic processes, while subspace methods have largely been based on developments in numerical linear algebra and matrix approximation theory.
This book bridges the gap between these two classes of methods by showing how the ideas behind subspace methods can be incorporated into traditional linear filtering. In the context of subspace methods, the enhancement problem can then be seen as a classical linear filter design problem. This means that various solutions can more easily be compared and their performance bounded and assessed in terms of noise reduction and speech distortion. The book shows how various filter designs can be obtained in this framework, including the maximum SNR, Wiener, LCMV, and MVDR filters, and how these can be applied in various contexts, like in single-channel and multichannel speech enhancement, and in both the time and frequency domains.
- First short book treating subspace approaches in a unified way for time and frequency domains, single-channel, multichannel, as well as binaural, speech enhancement
- Bridges the gap between optimal filtering methods and subspace approaches
- Includes original presentation of subspace methods from different perspectives
Signal Processing researchers and R&D engineers in industry
Chapter 1. Introduction
Abstract
1.1 History and Applications of Subspace Methods
1.2 Speech Enhancement from a Signal Subspace Perspective
1.3 Scope and Organization of the Work
References
Chapter 2. General Concept with the Diagonalization of the Speech Correlation Matrix
Abstract
2.1 Signal Model and Problem Formulation
2.2 Linear Filtering with a Rectangular Matrix
2.3 Performance Measures
2.4 Optimal Rectangular Filtering Matrices
References
Chapter 3. General Concept with the Joint Diagonalization of the Speech and Noise Correlation Matrices
Abstract
3.1 Signal Model and Problem Formulation
3.2 Linear Filtering with a Rectangular Matrix
3.3 Performance Measures
3.4 Optimal Rectangular Filtering Matrices
3.5 Another Signal Model
References
Chapter 4. Single-Channel Speech Enhancement in the Time Domain
Abstract
4.1 Signal Model and Problem Formulation
4.2 Linear Filtering with a Rectangular Matrix
4.3 Performance Measures
4.4 Optimal Rectangular Filtering Matrices
4.5 Single-Channel Noise Reduction Revisited
References
Chapter 5. Multichannel Speech Enhancement in the Time Domain
Abstract
5.1 Signal Model and Problem Formulation
5.2 Linear Filtering with a Rectangular Matrix
5.3 Performance Measures
5.4 Optimal Rectangular Filtering Matrices
References
Chapter 6. Multichannel Speech Enhancement in the Frequency Domain
Abstract
6.1 Signal Model and Problem Formulation
6.2 Linear Array Model
6.3 Performance Measures
6.4 Optimal Filters
References
Chapter 7. A Bayesian Approach to the Speech Subspace Estimation
Abstract
7.1 Signal Model and Problem Formulation
7.2 Estimation Based on the Minimum Mean-Square Distance
7.3 A Closed-Form Solution Based on the Bingham Posterior
References
Chapter 8. Evaluation of the Time-Domain Speech Enhancement Filters
Abstract
8.1 Evaluation of Single-Channel Filters
8.2 Evaluation of Multichannel Filters
References
Index
- No. of pages: 138
- Language: English
- Edition: 1
- Published: January 4, 2014
- Imprint: Academic Press
- Paperback ISBN: 9780128001394
- eBook ISBN: 9780128002537
JB
Jacob Benesty
JJ
Jesper Rindom Jensen
MC
Mads Graesboll Christensen
JC