In this paper we propose a deep learning-based MVDR beamforming weight estimation method. The MVDR beamforming weight can be estimated based on deep learning using GCC-PHAT without the information on the source location, while the MVDR beamforming weight requires information on the source location. As a result of an experiment with REVERB challenge data, the root mean square error between the estimated weight and the MVDR weight was found to be 0.32.

Authors: Jo, Moon Ju; Lee, Geon Woo; Moon, Jung Min; Cho, Choongsang; Kim, Hong Kook
Affiliations: Gwangju Institute of Science and Technology (GIST), Gwangju, Korea; Artificial Intelligence Research Center, Korea Electronics Technology Institute (KETI), Sungnam, Korea(See document for exact affiliation information.)
AES Convention: 145 (October 2018) Paper Number: 10068
Publication Date: October 7, 2018 Import into BibTeX
Subject: Acoustics and Signal Processing
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=19794

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