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: Permutation Correction in the Frequency Domain in Blind Separation of Speech Mixtures | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 75206 Pages 1-16 DOI ASP 2006 75206 Permutation Correction in the Frequency Domain in Blind Separation of Speech Mixtures Ch. Serviere1 and D. T. Pham2 1 Laboratoire des Images et des Signaux BP 46 38402 St Martin d H ere Cedex France 2 Laboratoire de Modélisation et Calcul BP 53 38041 Grenoble Cedex France Received 31 January 2005 Revised 26 August 2005 Accepted 1 September 2005 This paper presents a method for blind separation of convolutive mixtures of speech signals based on the joint diagonalization of the time varying spectral matrices of the observation records. The main and still largely open problem in a frequency domain approach is permutation ambiguity. In an earlier paper of the authors the continuity of the frequency response of the unmixing filters is exploited but it leaves some frequency permutation jumps. This paper therefore proposes a new method based on two assumptions. The frequency continuity of the unmixing filters is still used in the initialization of the diagonalization algorithm. Then the paper introduces a new method based on the time-frequency representations of the sources. They are assumed to vary smoothly with frequency. This hypothesis of the continuity of the time variation of the source energy is exploited on a sliding frequency bandwidth. It allows us to detect the remaining frequency permutation jumps. The method is compared with other approaches and results on real world recordings demonstrate superior performances of the proposed algorithm. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Blind source separation consists in extracting independent sources from their mixtures without relying on any specific knowledge of the sources. Earlier works have been focused on linear instantaneous mixtures and several efficient algorithms have been developed. The problem is much more difficult in .
