Optimization Approach for the Cocktail Party Problem
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Optimization Approach for the Cocktail Party Problem
Superchapter: Joint Chapter of Communications, Information Theory, and Signal Processing Societies
A Joint Attention Decoding and Adaptive Beamforming Optimization Approach for the Cocktail Party Problem
The cocktail party problem has remained to be one of the most difficult problems for hearing devices even after decades of extensive research. One of the key challenges is to determine the desired talker in a cocktail party. Recently, researchers have successfully demonstrated the decoding of auditory attention using EEG, MEG or EMG. In addition, several research studies have attempted to incorporate the decoded auditory attention information into speech enhancement solutions. However, the existing solutions are less optimal in the sense that auditory attention decoding is often separate from speech enhancement. In this talk, we propose a joint auditory attention decoding and multi-channel speech enhancement approach. The proposed approach eliminates the need of extracting speech envelope of each talk, which is a difficult problem in practice by itself. Furthermore, the proposed solution is optimal in the sense that the attended talker’s speech is optimized using both microphone inputs and EEG inputs in a united framework. We present preliminary results to demonstrate the effectiveness of the algorithm and discuss future research directions.
Date and Time
- Date: 26 Aug 2019
- Time: 04:00 PM to 05:00 PM
- All times are Canada/Eastern
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Location
- 1280 Main Street West
- Hamilton, Ontario
- Canada L8S 4K1
- Building: ITB
- Room Number: A113
Contact
- Email event contact
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Dr. Jun Chen, Chair Hamilton Superchapter
Speakers
Dr. Tao Zhang of Starkey Laboratories, Inc.
A Joint Attention Decoding and Adaptive Beamforming Optimization Approach for the Cocktail Party Problem
