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Signal enhancement with variable span linear filters

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Signal enhancement with variable span linear filters/ by Jacob Benesty, Mads G. Christensen, Jesper R. Jensen.
Author:	Benesty, Jacob.
other author:	Christensen, Mads G.
Published:	Singapore :Springer Singapore : : 2016.,
Description:	ix, 172 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Signal processing. -
Online resource:	http://dx.doi.org/10.1007/978-981-287-739-0
ISBN:	9789812877390

Signal enhancement with variable span linear filters
Benesty, Jacob.

Signal enhancement with variable span linear filters[electronic resource] /by Jacob Benesty, Mads G. Christensen, Jesper R. Jensen. - Singapore :Springer Singapore :2016. - ix, 172 p. :ill., digital ;24 cm. - Springer topics in signal processing,71866-2609 ;. - Springer topics in signal processing ;v.10..

Introduction -- General Concept with Filtering Vectors -- General Concept with Filtering Matrices -- Single-Channel Signal Enhancement in the STFT Domain -- Multichannel Signal Enhancement in the Time Domain -- Multichannel Signal Enhancement in the STFT Domain -- Binaural Signal Enhancement in the Time Domain.

This book introduces readers to the novel concept of variable span speech enhancement filters, and demonstrates how it can be used for effective noise reduction in various ways. Further, the book provides the accompanying Matlab code, allowing readers to easily implement the main ideas discussed. Variable span filters combine the ideas of optimal linear filters with those of subspace methods, as they involve the joint diagonalization of the correlation matrices of the desired signal and the noise. The book shows how some well-known filter designs, e.g. the minimum distortion, maximum signal-to-noise ratio, Wiener, and tradeoff filters (including their new generalizations) can be obtained using the variable span filter framework. It then illustrates how the variable span filters can be applied in various contexts, namely in single-channel STFT-based enhancement, in multichannel enhancement in both the time and STFT domains, and, lastly, in time-domain binaural enhancement. In these contexts, the properties of these filters are analyzed in terms of their noise reduction capabilities and desired signal distortion, and the analyses are validated and further explored in simulations.

ISBN: 9789812877390

Standard No.: 10.1007/978-981-287-739-0doiSubjects--Topical Terms:

561459
Signal processing.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

Signal enhancement with variable span linear filters
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245 1 0 $a Signal enhancement with variable span linear filters $h [electronic resource] / $c by Jacob Benesty, Mads G. Christensen, Jesper R. Jensen.
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490 1 $a Springer topics in signal processing, $x 1866-2609 ; $v 7
505 0 $a Introduction -- General Concept with Filtering Vectors -- General Concept with Filtering Matrices -- Single-Channel Signal Enhancement in the STFT Domain -- Multichannel Signal Enhancement in the Time Domain -- Multichannel Signal Enhancement in the STFT Domain -- Binaural Signal Enhancement in the Time Domain.
520 $a This book introduces readers to the novel concept of variable span speech enhancement filters, and demonstrates how it can be used for effective noise reduction in various ways. Further, the book provides the accompanying Matlab code, allowing readers to easily implement the main ideas discussed. Variable span filters combine the ideas of optimal linear filters with those of subspace methods, as they involve the joint diagonalization of the correlation matrices of the desired signal and the noise. The book shows how some well-known filter designs, e.g. the minimum distortion, maximum signal-to-noise ratio, Wiener, and tradeoff filters (including their new generalizations) can be obtained using the variable span filter framework. It then illustrates how the variable span filters can be applied in various contexts, namely in single-channel STFT-based enhancement, in multichannel enhancement in both the time and STFT domains, and, lastly, in time-domain binaural enhancement. In these contexts, the properties of these filters are analyzed in terms of their noise reduction capabilities and desired signal distortion, and the analyses are validated and further explored in simulations.
650 0 $a Signal processing. $3 561459
650 0 $a Noise control. $3 562285
650 1 4 $a Engineering. $3 561152
650 2 4 $a Signal, Image and Speech Processing. $3 670837
700 1 $a Christensen, Mads G. $3 1106044
700 1 $a Jensen, Jesper R. $3 1106045
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