Cocktail Party Problem as Binary Classification

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• Prof DeLiang Wang, Ohio State University, USA
• Friday 04 September 2009, 14:00-15:00
• Gabor Seminar Room, 611.

If you have a question about this talk, please contact Danilo Mandic.

Speech segregation, or the cocktail party problem, has proven to be extremely challenging. Part of the challenge stems from the lack of a carefully analyzed computational goal. While the separation of every sound source in a mixture is considered the gold standard, I argue that such an objective is neither realistic nor what the human auditory system does. Motivated by the auditory masking phenomenon, we have suggested instead the ideal time-frequency (T-F) binary mask as a main goal for computational auditory scene analysis. Ideal binary masking retains the mixture energy in T-F units where the local signal-to-noise ratio exceeds a certain threshold, and rejects the mixture energy in other T-F units. Recent psychophysical evidence shows that ideal binary masking leads to large speech intelligibility improvements in noisy environments for both normal-hearing and hearing-impaired listeners. The effectiveness of the ideal binary mask implies that sound separation may be formulated as a case of binary classification, which opens the cocktail party problem to a variety of pattern classification and clustering methods. As an example, I discuss a recent system that segregates unvoiced speech by supervised classification of acoustic-phonetic features.

Biography: DeLiang Wang received the B.S. degree in 1983 and the M.S. degree in 1986 from Peking (Beijing) University and the Ph.D. degree in 1991 from the University of Southern California. Since 1991, he has been with the Department of Computer Science & Engineering and the Center for Cognitive Science at The Ohio State University, where he is a Professor. He has also been a visiting scholar at Harvard University and Oticon A/S. Among his honors are the U.S. Office of Naval Research Young Investigator Award in 1996 and the Helmholtz Award from the International Neural Network Society in 2008. He is an IEEE Fellow.

This talk is part of the COMMSP Seminar series.

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