Blind‐Matched Filtering for Speech Enhancement with Distributed Microphones

Sebastian Stenzel,Jürgen Freudenberger

doi:10.1155/2012/169853

Abstract

A multichannel noise reduction and equalization approach for distributed microphones is presented. The speech enhancement is based on a blind‐matched filtering algorithm that combines the microphone signals such that the output SNR is maximized. The algorithm is developed for spatially uncorrelated but nonuniform noise fields, that is, the noise signals at the different microphones are uncorrelated, but the noise power spectral densities can vary. However, no assumptions on the array geometry are made. The proposed method will be compared to the speech distortion‐weighted multichannel Wiener filter (SDW‐MWF). Similar to the SDW‐MWF, the new algorithm requires only estimates of the input signal to noise ratios and the input cross‐correlations. Hence, no explicit channel knowledge is necessary. A new version of the SDW‐MWF for spatially uncorrelated noise is developed which has a reduced computational complexity, because matrix inversions can be omitted. The presented blind‐matched filtering approach is similar to this SDW‐MWF for spatially uncorrelated noise but additionally achieves some improvements in the speech quality due to a partial equalization of the acoustic system.

Highlights

In many speech communication systems, like hands-free car kits, teleconferencing systems, and speech recognition systems, the desired speech signal is linearly distorted by the room acoustics and corrupted by undesired background noise
The proposed method will be compared to the speech distortion-weighted multichannel Wiener filter (SDW-MWF)
We show that combined with a single channel Wiener filter, this new structure is equivalent with the SDW-MWF with respect to noise suppression

Summary

Introduction

In many speech communication systems, like hands-free car kits, teleconferencing systems, and speech recognition systems, the desired speech signal is linearly distorted by the room acoustics and corrupted by undesired background noise. Efficient speech processing techniques are required to enhance the speech signal under the constraint of a small speech distortion. The use of multiple microphones can improve the performance compared to single microphone systems [1]. The most common way to place the microphones is beamformer arrays with designed array geometry. Beamforming algorithms exploit the spatial directivity effects by a proper combining, like the Frost beamformer [2] or the generalized sidelobe canceler (GSC) [3]. The microphones are located in close proximity and the same signal conditions at the microphone positions are assumed

Objectives

Results

Conclusion