Speech enhancement [recurso electrónico] : a signal subspace perspective / Jacob Benesty ... [et al.].

Colaborador(es): Benesty, JacobTipo de material: Texto

TextoDetalles de publicación: Oxford ; Waltham, MA : Academic Press, 2014Edición: 1st edDescripción: 1 online resource (1 v.) : illTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 1306315131 (electronic bk.); 9781306315135 (electronic bk.); 9780128002537 (electronic bk.); 0128002530 (electronic bk.); 0128001399; 9780128001394Tema(s): Speech processing systems | Signal processing | Speech processing systems | Signal processingGénero/Forma: Electronic books. | Electronic books.Formatos físicos adicionales: Print version:: Sin títuloClasificación CDD: 006.4/5 Clasificación LoC:TK7882.S65 | S744 2014Recursos en línea: Libro electrónico ScienceDirect Texto

Contenidos:

Chapter 1. Introduction -- chapter 2. General concept with the diagonalization of the speech correlation matrix -- chapter 3. General concept with the joint diagonalization of the speech and noise correlation matrices -- chapter 4. Single-channel speech enhancement in the time domain -- chapter 5. Multichannel speech enhancement in the time domain -- chapter 6. Multichannel speech enhancement in the frequency domain -- chapter 7. A Bayesian approach to the speech subspace estimation -- chapter 8. Evaluation of the time-domain speech enhancement filters.

Resumen: 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 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.

Existencias ( 1 )
Notas de título ( 5 )

Existencias
Tipo de ítem	Biblioteca actual	Colección	Signatura	Copia número	Estado	Fecha de vencimiento	Código de barras
Libro Electrónico	Biblioteca Electrónica	Colección de Libros Electrónicos	TK7882 .S65 S744 2014 (Browse shelf(Abre debajo))	1	No para préstamo		380077-2001

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Includes bibliographical references and index.

Description based on online resource; title from title page (Safari, viewed Feb. 6, 2014).

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 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.