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 Fast Noise Compensation and Adaptive Enhancement for Speech Separation | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2008 Article ID 349214 10 pages doi 2008 349214 Research Article Fast Noise Compensation and Adaptive Enhancement for Speech Separation Rong Hu and Yunxin Zhao Department of Computer Science University of Missouri-Columbia Columbia MO 65211 USA Correspondence should be addressed to Yunxin Zhao zhaoy@ Received 4 December 2007 Revised 12 March 2008 Accepted 12 May 2008 Recommended by D. Wang We propose a novel approach to improve adaptive decorrelation filtering- ADF- based speech source separation in diffuse noise. The effects of noise on system adaptation and separation outputs are handled separately. First fast noise compensation NC is developed for adaptation of separation filters forcing ADF to focus on source separation next output noises are suppressed by speech enhancement. By tracking noise components in output cross-correlation functions the bias effect of noise on the system adaptation objective function is compensated and by adaptively estimating output noise autocorrelations the speech separation output is enhanced. For fast noise compensation a blockwise fast ADF FADF is implemented. Experiments were conducted on real and simulated diffuse noises. Speech mixtures were generated by convolving TIMIT speech sources with acoustic path impulse responses measured in a real room with reverberation time T60 second. The proposed techniques significantly improved separation performance and phone recognition accuracy of ADF outputs. Copyright 2008 R. Hu and Y. Zhao. 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 Interference speech and diffuse noise present double folds of challenges for hands-free automatic speech recognition ASR and speech communication. For practical .
