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关于俄亥俄州立大学汪德亮教授学术报告的通知

  讲座题目:

  On Generalization of Classification-based Speech Separation

  主讲嘉宾:DeLiang Wang (Professor,The Ohio State University)

  时 间:2013年7月18日下午14:30

  地 点:科大西区科技实验西楼一楼117会议室

  主办单位:语音及语言信息处理国家工程实验室

  报告摘要:

  Speech separation, popularly known as the cocktail party problem, is a widely acknowledged challenge in speech and signal processing. Motivated by advances in speech perception and computational auditory scene analysis, we have suggested a new formulation to this problem that classifies time-frequency units into two classes: those dominated by the target speech and the rest. Recent separation algorithms that adopt this supervised classification formulation show considerable promise for solving the speech separation problem. In supervised learning, a paramount issue is generalization to conditions unseen during training. This presentation describes novel methods to deal with the generalization issue where support vector machines (SVMs) are used to estimate the ideal binary mask (IBM). One method employs distribution fitting to adapt to unseen signal-to-noise ratios and iterative voice activity detection to adapt to unseen noises. Another method learns more linearly separable features using deep neural networks (DNNs) and then couples DNN and linear SVM for training on a variety of noisy conditions. Systematic evaluations show high quality IBM estimation in new acoustic environments.

  嘉宾简介:

  DeLiang Wang received the B.S. degree and the M.S. degree from Peking (Beijing) University and the Ph.D. degree in 1991 from the University of Southern California all in computer science. Since 1991, he has been with the Department of Computer Science & Engineering and the Center for Cognitive and Brain Sciences at The Ohio State University, where he is a Professor. He was a visiting scholar in the Department of Psychology at Harvard University from 1998 to 1999, and at Oticon A/S in Denmark from 2006 to 2007. Wang's research interests include machine perception and neurodynamics. He received the Office of Naval Research Young Investigator Award in 1996, the 2005 Outstanding Paper Award from IEEE Transactions on Neural Networks, and the 2008 Helmholtz Award from the International Neural Network Society. He is an IEEE Fellow, and currently serves as Co-Editor-in-Chief of Neural Networks.