2017年
BAYESIAN MULTICHANNEL NONNEGATIVE MATRIX FACTORIZATION FOR AUDIO SOURCE SEPARATION AND LOCALIZATION
2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
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- 開始ページ
- 551
- 終了ページ
- 555
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/ICASSP.2017.7952216
- 出版者・発行元
- IEEE
This paper presents a Bayesian extension of multichannel nonnegative matrix factorization (MNMF) that decomposes the complex spectrograms of mixture signals recorded by a microphone array into basis spectra, their temporal activations, and the spatial correlation matrices of sources (directions) in the time-frequency-channel domain. Although the original MNMF can be used in a blind setting, prior knowledge of a microphone array is useful for improving source separation. The impulse response (spatial correlation matrix) of each direction can be measured in an anechoic room, however, it differs from that in a real environment where the microphone array is used. To solve this, we propose a unified Bayesian model of source separation and localization by introducing a prior distribution determined by an anechoic spatial correlation matrix on areal spatial correlation matrix with respect to each direction. This enables us to adaptively estimate areal spatial correlation matrix and the direction of each source. Experimental results showed that our method outperformed the original MNMF and the state-of-the-art methods with prior knowledge in terms of signal-to-distortion ratio (SDR) even when the method was used in an unknown environment with acoustic characteristics different from those of the anechoic room.
- リンク情報
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- DOI
- https://doi.org/10.1109/ICASSP.2017.7952216
- DBLP
- https://dblp.uni-trier.de/rec/conf/icassp/ItakuraBNIYK17
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000414286200111&DestApp=WOS_CPL
- URL
- http://dblp.uni-trier.de/db/conf/icassp/icassp2017.html#conf/icassp/ItakuraBNIYK17
- ID情報
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- DOI : 10.1109/ICASSP.2017.7952216
- ISSN : 1520-6149
- DBLP ID : conf/icassp/ItakuraBNIYK17
- Web of Science ID : WOS:000414286200111