Speech enhancement in the Karhunen-Loève expansion domain
By: Jacob Benesty, Jingdong Chen, Yiteng Huang
Contributors: Chen, J. (Jingdong); Huang, Yiteng, 1972-
Series: Synthesis lectures on speech and audio processing -- # 7
Publisher: Morgan & Claypool
Published: 2011
Place of publication: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA)
Language: eng
Format: [electronic resource] /
ISBN-13: 9781608456055; 9781608456048
Subjects: Speech processing systems; Time-domain analysis; Digital filters (Mathematics); Signal processing; Digital techniques
Other titles: Synthesis digital library of engineering and computer science.
Description: This book is devoted to the study of the problem of speech enhancement whose objective is the recovery of a signal of interest (i.e., speech) from noisy observations. Typically, the recovery process is accomplished by passing the noisy observations through a linear filter (or a linear transformation). Since both the desired speech and undesired noise are filtered at the same time, the most critical issue of speech enhancement resides in how to design a proper optimal filter that can fully take advantage of the difference between the speech and noise statistics to mitigate the noise effect as much as possible while maintaining the speech perception identical to its original form. The optimal filters can be designed either in the time domain or in a transform space. As the title indicates, this book will focus on developing and analyzing optimal filters in the Karhunen-Loève expansion (KLE) domain. We begin by describing the basic problem of speech enhancement and the fundamental principles to solve it in the time domain. We then explain how the problem can be equivalently formulated in the KLE domain. Next, we divide the general problem in the KLE domain into four groups, depending on whether interframe and interband information is accounted for, leading to four linear models for speech enhancement in the KLE domain. For each model, we introduce signal processing measures to quantify the performance of speech enhancement, discuss the formation of different cost functions, and address the optimization of these cost functions for the derivation of different optimal filters. Both theoretical analysis and experiments will be provided to study the performance of these filters and the links between the KLE-domain and time-domain optimal filters will be examined.
Notes: Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 91-95) and index.

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Table of contents: title: 1. Introduction; title: The problem of speech enhancement; title: Organization of the book; title: 2. Problem formulation; title: Signal model; title: Karhunen-Loève expansion (KLE); title: 3. Optimal filters in the time domain; title: Performance measures; title: Mean-square error (MSE) criterion; title: Wiener filter; title: Tradeoff filters; title: Subspace-type filter; title: Maximum signal-to-noise ratio (SNR) filter; title: 4. Linear models for signal enhancement in the KLE domain; title: Model 1; title: Model 2; title: Model 3; title: Model 4; title: 5. Optimal filters in the KLE domain with model 1; title: Performance measures; title: MSE criterion; title: Wiener filter; title: Tradeoff filter; title: Maximum SNR filter; title: 6. Optimal filters in the KLE domain with model 2; title: Performance measures; title: Maximum SNR filter; title: MSE criterion; title: Wiener filter; title: Minimum variance distortionless response (MVDR) filter; title: Tradeoff filter; title: 7. Optimal filters in the KLE domain with model 3; title: Performance measures; title: MSE criterion; title: Wiener filter; title: Tradeoff filter; title: Maximum SNR filter; title: 8. Optimal filters in the KLE domain with model 4; title: Performance measures; title: MSE criterion; title: Wiener filter; title: Tradeoff filter; title: MVDR filter; title: Maximum SNR filter; title: 9. Experimental study; title: Experimental conditions; title: Estimation of the correlation matrices and vectors; title: Performance measures; title: Performance of the time-domain filters; title: Wiener filter; title: Tradeoff filter; title: Performance of the KLE-domain filters with model 1; title: KLE-domain wiener filter; title: KLE-domain tradeoff filter; title: Performance of the KLE-domain filters with model 3; title: Performance of the KLE-domain filters with model 2; title: KLE-domain filters with model 4; title: Bibliography; title: Authors' biographies; title: Index.