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: Frequency-Domain Blind Source Separation of Many Speech Signals Using Near-Field and Far-Field Models | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 83683 Pages 1-13 DOI ASP 2006 83683 Frequency-Domain Blind Source Separation of Many Speech Signals Using Near-Field and Far-Field Models Ryo Mukai Hiroshi Sawada Shoko Araki and Shoji Makino NTT Communication Science Laboratories NTT Corporation 2-4 Hikaridai Seika-Cho Soraku-Gun Kyoto 619-0237 Japan Received 19 December 2005 Revised 26 April 2006 Accepted 11 June 2006 We discuss the frequency-domain blind source separation BSS of convolutive mixtures when the number of source signals is large and the potential source locations are omnidirectional. The most critical problem related to the frequency-domain BSS is the permutation problem and geometric information is helpful as regards solving it. In this paper we propose a method for obtaining proper geometric information with which to solve the permutation problem when the number of source signals is large and some of the signals come from the same or a similar direction. First we describe a method for estimating the absolute DOA by using relative DOAs obtained by the solution provided by independent component analysis ICA and the far-field model. Next we propose a method for estimating the spheres on which source signals exist by using ICA solution and the near-field model. We also address another problem with regard to frequency-domain BSS that arises from the circularity of discrete-frequency representation. We discuss the characteristics of the problem and present a solution for solving it. Experimental results using eight microphones in a room show that the proposed method can separate a mixture of six speech signals arriving from various directions even when two of them come from the same direction. Copyright 2006 Ryo Mukai 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 .
