DeLiang Wang (OSU) - A Classification Approach to the Cocktail Party Problem
The cocktail party problem, also known as the speech segregation problem, has evaded a solution for decades in speech and audio processing. Motivated by recent advances in psychoacoustics and computational auditory scene analysis, I will advocate a new formulation to this old problem: instead of aiming at extracting the target speech, it classifies time-frequency units into two classes: those dominated by the target speech and the rest. This new formulation shifts the emphasis from signal estimation to signal classification, with an important implication that the cocktail party problem is now open to a plethora of binary classification techniques in modern machine learning. I will discuss recent speech segregation algorithms that adopt the binary classification formulation, and these systems represent considerable progress towards solving the cocktail party problem.
DeLiang Wang received the B.S. degree in 1983 and the M.S. degree in 1986 from Peking (Beijing) University, Beijing, China, and the Ph.D. degree in 1991 from the University of Southern California, Los Angeles, CA, all in computer science. Since 1991, he has been with the Department of Computer Science & Engineering and the Center for Cognitive Science at The Ohio State University, Columbus, OH, where he is a Professor. From 1998 to 1999, he was a visiting scholar in the Department of Psychology at Harvard University. From 2006 to 2007, he was a visiting scholar at Oticon A/S, Denmark. Wang's research interests include machine perception and neurodynamics. He serves as Co-Editor-in-Chief of Neural Networks. 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.