Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Postfiltering Using Multichannel Spectral Estimation in Multispeaker Environments | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 860360 10 pages doi 2008 860360 Research Article Postfiltering Using Multichannel Spectral Estimation in Multispeaker Environments Hai Quang Dam Sven Nordholm Hai Huyen Dam and Siow Yong Low Western Australian Telecommunications Research Institute WATRI Crawley WA 6009 Australia Correspondence should be addressed to Hai Quang Dam amhai@ Received 14 September 2006 Accepted 5 July 2007 Recommended by Douglas O Shaughnessy This paper investigates the problem of enhancing a single desired speech source from a mixture of signals in multispeaker environments. A beamformer structure is proposed which combines a fixed beamformer with postfiltering. In the first stage the fixed multiobjective optimal beamformer is designed to spatially extract the desired source by suppressing all other undesired sources. In the second stage a multichannel power spectral estimator is proposed and incorporated in the postfilter thus enabling further suppression capability. The combined scheme exploits both spatial and spectral characteristics of the signals. Two new multichannel spectral estimation methods are proposed for the postfiltering using respectively inner product and joint diagonalization. Evaluations using recordings from a real-room environment show that the proposed beamformer offers a good interference suppression level whilst maintaining a low-distortion level of the desired source. Copyright 2008 Hai Quang Dam et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION Multichannel beamforming techniques can be largely divided into three types namely fixed optimum and adaptive beamforming 1 2 . For a fixed beamformer the beamformer weights which usually consist of FIR-filter weights .
