2018年5月2日学术报告（DeLiang Wang，美国俄亥俄州立大学）
类别：未知 发布人：admin 浏览次数： 次 发布时间：2018-04-24 15:03
报告题目：  Deep Learning Based Speech Separation
报告人：    汪德亮 (DeLiang Wang)，博士，教授, 博导
报告人单位：美国俄亥俄州立大学计算机科学与工程系，认知与脑科学中心
时间：      2018年5月2日上午9:30
地点：      外围投注365B-403报告厅

报告人简介：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 and University Distinguished Scholar. He also holds a visiting appointment at the Center of Intelligent Acoustics and Immersive Communications, Northwestern Polytechnical University. 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.
 
报告摘要:Speech separation, or the cocktail party problem, has evaded a solution for decades in speech and audio processing. Motivated by auditory perception, I have been advocating a new formulation to this old challenge that estimates an ideal time-frequency mask (binary or ratio). This new formulation has an important implication that the speech separation problem is open to modern machine learning techniques, and deep neural networks (DNNs) are particularly well-suited for this task due to their representational capacity. I will describe recent algorithms that employ DNNs for supervised speech separation, including speech enhancement and speaker separation. DNN-based mask estimation elevates speech separation performance to new levels, and produces the first demonstration of substantial speech intelligibility improvements for both hearing-impaired and normal-hearing listeners in background interference. These advances represent major progress towards solving the cocktail party problem.
 
邀请人：胡瑞敏教授，涂卫平副教授
上一篇：2018年4月24日学术报告（Zhaofeng Su，University of Sydney）
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