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Speech Enhancement : a Signal Subspace Perspective.

Author: Jacob Benesty; Jesper Rindom Jensen; Mads Graesboll Christensen; J Chen
Publisher: Burlington : Elsevier Science, 2014.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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, 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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Benesty, Jacob.
Speech Enhancement : A Signal Subspace Perspective.
Burlington : Elsevier Science, ©2014
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jacob Benesty; Jesper Rindom Jensen; Mads Graesboll Christensen; J Chen
ISBN: 9780128002537 0128002530
OCLC Number: 868489114
Description: 1 online resource (143 pages)
Contents: Half Title; Title Page; Copyright; Contents; 1 Introduction; 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; 2 General Concept with the Diagonalization of the Speech Correlation Matrix; 2.1 Signal Model and Problem Formulation; 2.2 Linear Filtering with a Rectangular Matrix; 2.3 Performance Measures; 2.3.1 Noise Reduction; 2.3.2 Speech Distortion; 2.3.3 MSE Criterion; 2.4 Optimal Rectangular Filtering Matrices; 2.4.1 Maximum SNR; 2.4.2 Wiener; 2.4.3 MVDR; 2.4.4 Tradeoff. 2.4.5 LCMVReferences; 3 General Concept with the Joint Diagonalization of the Speech and Noise Correlation Matrices; 3.1 Signal Model and Problem Formulation; 3.2 Linear Filtering with a Rectangular Matrix; 3.3 Performance Measures; 3.3.1 Noise Reduction; 3.3.2 Speech Distortion; 3.3.3 MSE Criterion; 3.4 Optimal Rectangular Filtering Matrices; 3.4.1 Maximum SNR; 3.4.2 Wiener; 3.4.3 MVDR; 3.4.4 Tradeoff; 3.5 Another Signal Model; References; 4 Single-Channel Speech Enhancement in the Time Domain; 4.1 Signal Model and Problem Formulation; 4.2 Linear Filtering with a Rectangular Matrix. 4.3 Performance Measures4.4 Optimal Rectangular Filtering Matrices; 4.5 Single-Channel Noise Reduction Revisited; 4.5.1 Orthogonal Decomposition; 4.5.2 Linear Filtering with a Rectangular Matrix; 4.5.3 Performance Measures; 4.5.4 Optimal Rectangular Filtering Matrices; References; 5 Multichannel Speech Enhancement in the Time Domain; 5.1 Signal Model and Problem Formulation; 5.2 Linear Filtering with a Rectangular Matrix; 5.3 Performance Measures; 5.3.1 Noise Reduction; 5.3.2 Speech Distortion; 5.3.3 MSE Criterion; 5.4 Optimal Rectangular Filtering Matrices; 5.4.1 Maximum SNR; 5.4.2 Wiener. 5.4.3 MVDR5.4.4 Tradeoff; 5.4.5 LCMV; References; 6 Multichannel Speech Enhancement in the Frequency Domain; 6.1 Signal Model and Problem Formulation; 6.2 Linear Array Model; 6.3 Performance Measures; 6.3.1 Noise Reduction; 6.3.2 Speech Distortion; 6.3.3 MSE Criterion; 6.4 Optimal Filters; 6.4.1 Maximum SNR; 6.4.2 Wiener; 6.4.3 MVDR; 6.4.4 Tradeoff; 6.4.5 LCMV; References; 7 A Bayesian Approach to the Speech Subspace Estimation; 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.

Abstract:

Speech enhancement is a classical problem in signal processing. Two of the conventional approaches for solving this problem are linear filtering, like the classical Wiener filter, and subspace  Read more...

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