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 A Computational Auditory Scene Analysis-Enhanced Beamforming Approach for Sound Source Separation | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 403681 17 pages doi 2009 403681 Research Article A Computational Auditory Scene Analysis-Enhanced Beamforming Approach for Sound Source Separation L. A. Drake 1 J. C. Rutledge 2 J. Zhang 3 and A. Katsaggelos EURASIP Member 4 1JunTech Inc. 2314 E. Stratford Ct Shorewood WI53211 USA 2 Computer Science and Electrical Engineering Department University of Maryland Baltimore County Baltimore MD 21250 USA 3 Electrical Engineering and Computer Science Department University of Wisconsin-Milwaukee Milwaukee WI53201 USA 4 Department of Electrical Engineering and Computer Science Northwestern University Evanston IL 60208 USA Correspondence should be addressed to L. A. Drake ladrake@ Received 1 December 2008 Revised 18 May 2009 Accepted 12 August 2009 Recommended by Henning Puder Hearing aid users have difficulty hearing target signals such as speech in the presence of competing signals or noise. Most solutions proposed to date enhance or extract target signals from background noise and interference based on either location attributes or source attributes. Location attributes typically involve arrival angles at a microphone array. Source attributes include characteristics that are specific to a signal such as fundamental frequency or statistical properties that differentiate signals. This paper describes a novel approach to sound source separation called computational auditory scene analysis-enhanced beamforming CASA-EB that achieves increased separation performance by combining the complementary techniques of CASA a source attribute technique with beamforming a location attribute technique complementary in the sense that they use independent attributes for signal separation. CASA-EB performs sound source separation by temporally and spatially filtering a multichannel input signal and then grouping the resulting signal components into separated signals based on
