Speech Enhancement
A Signal Subspace Perspective
(Sprache: Englisch)
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...
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Klappentext zu „Speech Enhancement “
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
Inhaltsverzeichnis zu „Speech Enhancement “
1. Introduction 2. General Concept with the Diagonalization of the Speech Correlation Matrix 3. General Concept with the Joint Diagonalization of the Speech and Noise Correlation Matrices 4. Single-Channel Speech Enhancement in the Time Domain 5. Multichannel Speech Enhancement in the Time Domain 6. Multichannel Speech Enhancement in the Frequency Domain 7. A Bayesian Approach to the Speech Subspace Estimation 8. Evaluation of the Time-Domain Speech Enhancement Filters
Autoren-Porträt von Jacob Benesty, Jesper Rindom Jensen, Mads Graesboll Christensen
Jacob Benesty received a Master degree in microwaves from Pierre & Marie Curie University, France, in 1987, and a Ph.D. degree in control and signal processing from Orsay University, France, in April 1991. During his Ph.D. (from Nov. 1989 to Apr. 1991), he worked on adaptive filters and fast algorithms at the Centre National d'Etudes des Telecomunications (CNET), Paris, France. From January 1994 to July 1995, he worked at Telecom Paris University on multichannel adaptive filters and acoustic echo cancellation. From October 1995 to May 2003, he was first a Consultant and then a Member of the Technical Staff at Bell Laboratories, Murray Hill, NJ, USA. In May 2003, he joined the University of Quebec, INRS-EMT, in Montreal, Quebec, Canada, as a Professor. His research interests are in signal processing, acoustic signal processing, and multimedia communications. He is the inventor of many important technologies. In particular, he was the lead researcher at Bell Labs who conceived and designed the world-first real-time hands-free full-duplex stereophonic teleconferencing system. Also, he conceived and designed the world-first PC-based multi-party hands-free full-duplex stereo conferencing system over IP networks. He was the co-chair of the 1999 International Workshop on Acoustic Echo and Noise Control and the general co-chair of the 2009 IEEEWorkshop on Applications of Signal Processing to Audio and Acoustics. He is the recipient, with Morgan and Sondhi, of the IEEE Signal Processing Society 2001 Best Paper Award. He is the recipient, with Chen, Huang, and Doclo, of the IEEE Signal Processing Society 2008 Best Paper Award. He is also the co-author of a paper for which Huang received the IEEE Signal Processing Society 2002 Young Author Best Paper Award. In 2010, he received the "Gheorghe Cartianu Award? from the Romanian Academy. In 2011, he received the Best Paper Award from the IEEE WASPAA for a paper that he co-authored with Jingdong Chen.
Bibliographische Angaben
- Autoren: Jacob Benesty , Jesper Rindom Jensen , Mads Graesboll Christensen
- 2014, 138 Seiten, Maße: 15,2 x 22,9 cm, Kartoniert (TB), Englisch
- Verlag: Academic Press
- ISBN-10: 0128001399
- ISBN-13: 9780128001394
- Erscheinungsdatum: 28.01.2014
Sprache:
Englisch
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