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 MAP-Based Underdetermined Blind Source Separation of Convolutive Mixtures by | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 24717 12 pages doi 2007 24717 Research Article MAP-Based Underdetermined Blind Source Separation of Convolutive Mixtures by Hierarchical Clustering and -Norm Minimization Stefan Winter 1 2 Walter Kellermann 2 Hiroshi Sawada 1 and Shoji Makino1 1NTT Communication Science Laboratories Nippon Telegraph and Telephone Corporation 2-4 Hikaridai Seika-Cho Soraku-Gun Kyoto 619-0237 Japan 2 Multimedia Communications and Signal Processing University of Erlangen-Nuremberg CauerstraBe 7 91058 Erlangen Germany Received 30 September 2005 Revised 24 January 2006 Accepted 11 June 2006 Recommended by Frank Ehlers We address the problem of underdetermined BSS. While most previous approaches are designed for instantaneous mixtures we propose a time-frequency-domain algorithm for convolutive mixtures. We adopt a two-step method based on a general maximum a posteriori MAP approach. In the first step we estimate the mixing matrix based on hierarchical clustering assuming that the source signals are sufficiently sparse. The algorithm works directly on the complex-valued data in the time-frequency domain and shows better convergence than algorithms based on self-organizing maps. The assumption of Laplacian priors for the source signals in the second step leads to an algorithm for estimating the source signals. It involves the 1 -norm minimization of complex numbers because of the use of the time-frequency-domain approach. We compare a combinatorial approach initially designed for real numbers with a second-order cone programming SOCP approach designed for complex numbers. We found that although the former approach is not theoretically justified for complex numbers its results are comparable to or even better than the SOCP solution. The advantage is a lower computational cost for problems with low input output dimensions. Copyright 2007 Stefan Winter et al. This is an open access
