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Semi‐blind source separation using binary masking and independent vector analysis
Author(s) -
Tachioka Yuuki,
Narita Tomohiro,
Ishii Jun
Publication year - 2015
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22072
Subject(s) - computer science , binary number , robustness (evolution) , speech recognition , independence (probability theory) , computation , blind signal separation , separation (statistics) , masking (illustration) , artificial intelligence , pattern recognition (psychology) , machine learning , algorithm , mathematics , statistics , telecommunications , art , biochemistry , chemistry , channel (broadcasting) , arithmetic , visual arts , gene
Recent prevalence of speech recognition system increases the opportunity of simultaneous recognition of multiple speakers' utterances. There are two types of source separation methods: physical and statistical. The former is based on the physical information such as a direction of arrival of sound sources. The latter only uses statistical independence. The advantage of the former is fast computation and effectiveness with precise information; and that of the latter is no need for physical information, which leads to the robustness of measurement errors. In this paper, we propose to combine these approaches effectively. Experiments on a speech recognition task show that the proposed method can achieve the upper limit performance of the two approaches. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.