tailieunhanh - Báo cáo hóa học: " Research Article Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions: Survey and Analysis"

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 Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions: Survey and Analysis | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 92953 24 pages doi 2007 92953 Research Article Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions Survey and Analysis Per Christian Hansen1 and S0ren Holdt Jensen2 1 Informatics and Mathematical Modelling Technical University of Denmark Building 321 2800 Lyngby Denmark 2 Department of Electronic Systems Aalborg University Niels Jernes Vej 12 9220 Aalborg Denmark Received 1 October 2006 Revised 18 February 2007 Accepted 31 March 2007 Recommended by Marc Moonen We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and in particular signal subspace techniques. The focus is on practical working algorithms using both diagonal eigenvalue and singular value decompositions and rank-revealing triangular decompositions ULV URV VSV ULLV and ULLIV . In addition we show how the subspace-based algorithms can be analyzed and compared by means of simple FIR filter interpretations. The algorithms are illustrated with working Matlab code and applications in speech processing. Copyright 2007 P. C. Hansen and S. H. Jensen. 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 The signal subspace approach has proved itself useful for signal enhancement in speech processing and many other applications see for example the recent survey 1 . The area has grown dramatically over the last 20 years along with advances in efficient computational algorithms for matrix computations 2-4 especially singular value decompositions and rank-revealing decompositions. The central idea is to approximate a matrix derived from the noisy data