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Speech Enhancement : a Signal Subspace Perspective.
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...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Burlington :
Elsevier Science,
2014.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 020 | |a 0128002530 |q (electronic bk.) | ||
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| 100 | 1 | |a Benesty, Jacob. | |
| 245 | 1 | 0 | |a Speech Enhancement : |b a Signal Subspace Perspective. |
| 260 | |a Burlington : |b Elsevier Science, |c 2014. | ||
| 300 | |a 1 online resource (143 pages) | ||
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| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 504 | |a References8 Evaluation of the Time-Domain Speech Enhancement Filters; 8.1 Evaluation of Single-Channel Filters; 8.1.1 Rank-Deficient Speech Correlation Matrix; 8.1.2 Full-Rank Speech Correlation Matrix; 8.2 Evaluation of Multichannel Filters; References; Index. | ||
| 520 | |a 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 processes, while subspace methods have largely been based on developments in numerical linear algebra and matrix approximation theory. This book bridges the gap between the. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Speech processing systems. | |
| 650 | 0 | |a Signal processing. | |
| 650 | 6 | |a Traitement automatique de la parole. | |
| 650 | 6 | |a Traitement du signal. | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Signal processing |2 fast | |
| 650 | 7 | |a Speech processing systems |2 fast | |
| 700 | 1 | |a Jensen, Jesper Rindom. | |
| 700 | 1 | |a Christensen, Mads Graesboll. | |
| 700 | 1 | |a Chen, J. |q (Jingdong) |1 https://id.oclc.org/worldcat/entity/E39PCjF4fk7Kf6ddkvgfPYwX3P | |
| 776 | 0 | 8 | |i Print version: |a Benesty, Jacob. |t Speech Enhancement : A Signal Subspace Perspective. |d Burlington : Elsevier Science, ©2014 |z 9780128001394 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1590525 |z Texto completo |
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