Optimal Time-Domain Noise Reduction Filters A Theoretical Study
Additive noise is ubiquitous in acoustics environments and can affect the intelligibility and quality of speech signals. Therefore, a so-called noise reduction algorithm is required to mitigate the effect of the noise that is picked up by the microphones.
- PDF / 1,075,815 Bytes
- 82 Pages / 439.37 x 666.142 pts Page_size
- 41 Downloads / 200 Views
For further volumes: http://www.springer.com/series/10059
Jacob Benesty Jingdong Chen •
Optimal Time-Domain Noise Reduction Filters A Theoretical Study
123
Prof. Dr. Jacob Benesty INRS-EMT University of Quebec de la Gauchetiere Ouest 800 Montreal, H5A 1K6, QC Canada e-mail: [email protected]
Jingdong Chen Northwestern Polytechnical University 127 Youyi West Road Xi’an, Shanxi 710072 China e-mail: [email protected]
ISSN 2191-8112
e-ISSN 2191-8120
ISBN 978-3-642-19600-3
e-ISBN 978-3-642-19601-0
DOI 10.1007/978-3-642-19601-0 Springer Heidelberg Dordrecht London New York Ó Jacob Benesty 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: eStudio Calamar, Berlin/Figueres Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Contents
1
Introduction . . . . . . . . . . . . . 1.1 Noise Reduction . . . . . . . 1.2 Organization of the Work . References . . . . . . . . . . . . . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
. . . .
1 1 2 2
2
Single-Channel Noise Reduction with a Filtering Vector . . . . . . 2.1 Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Linear Filtering with a Vector. . . . . . . . . . . . . . . . . . . . . . . 2.3 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Noise Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Speech Distortion . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Mean-Square Error (MSE) Criterion . . . . . . . . . . . . . 2.4 Optimal Filtering Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Maximum Signal-to-Noise Ratio (SNR). . . . . . . . . . . 2.4.2 Wiener. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Minimum Variance Distortionless Response (MVDR) . 2.4.4 Prediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.5 Tradeoff. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.6 Linearly Constrained Minimum Variance (LCMV) . . . 2.4.7 Practical
Data Loading...
