Prof. Dr. Nozomu Hamada

2016-02-05 17:46:01
Professor Dr. Nozomu Hamada

Universiti Teknologi Malaysia (UTM), Malaysia

Biography: Nozomu Hamada received his B.S., M.S. and Ph.D. degrees in electrical engineering from Keio University, Yokohama, Japan in 1970, 1972, and 1975 respectively. He served as a professor of the department of electrical engineering and the department of system design engineering, Keio University from 1991. He is currently a professor of department of electronic system engineering, Malaysia-Japan International Institute of Technology, Unitersiti Teknologi Malaysia, Kuala Lumpur from 2013. He was a visiting researcher at the Australian National University, an adjunct professor of Xi’an Jiaotong University and Xi’an Jiaotong University City College, and a visiting professor of Warsaw Technical University in 1982, 2006, and 2010 respectively. 
His research fields are digital signal processing, computer vision, and image processing. He is the author/co-author of 11 books. He was the chair of the IEEE Signal Processing Society, Japan Chapter in 2004, and was the chief editor of Journal of Signal Processing. 
Dr. Hamada received an excellent paper award of ICSPS 2014, best paper award of the Journal of Signal processing in 2012, and a life member of IEEE and a fellow of IEICE.

Title of Speech: Direction Finding and Sound Source Separation by Arbitrary Configuration Microphone Array

Abstract: The issues of direction-of-arrival (DOA) estimation of multiple sound sources and blind source separation of mixed speech signals are discussed. This talk focuses on a couple of recent research results which have been carried out in our group. At first, for the issue of source direction finding using a pair of microphones a phase-difference with probability density estimation approach is introduced. This will follow by discussing a general DOA estimation theory for arbitrary sensor configuration system. Second, for blind source separation problem, time-frequency masking algorithm utilizing a series of phase versus frequency data is introduced where the classification of time-frequency frames has been carried out. Finally, DOA estimation for moving sources as well as their separation is discussed.

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