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Independent Low-Rank Matrix Analysis Based on Generalized Kullback-Leibler Divergence
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Summary :
In this letter, we propose a new blind source separation method, independent low-rank matrix analysis based on generalized Kullback-Leibler divergence. This method assumes a time-frequency-varying complex Poisson distribution as the source generative model, which yields convex optimization in the spectrogram estimation. The experimental evaluation confirms the proposed method's efficacy.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.2 pp.458-463
- Publication Date
- 2019/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E102.A.458
- Type of Manuscript
- LETTER
- Category
- Engineering Acoustics
Authors
Shinichi MOGAMI
University of Tokyo
Yoshiki MITSUI
University of Tokyo
Norihiro TAKAMUNE
University of Tokyo
Daichi KITAMURA
Kagawa College
Hiroshi SARUWATARI
University of Tokyo
Yu TAKAHASHI
Yamaha Corporation
Kazunobu KONDO
Yamaha Corporation
Hiroaki NAKAJIMA
Yamaha Corporation
Hirokazu KAMEOKA
Nippon Telegraph and Telephone Corporation
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Shinichi MOGAMI, Yoshiki MITSUI, Norihiro TAKAMUNE, Daichi KITAMURA, Hiroshi SARUWATARI, Yu TAKAHASHI, Kazunobu KONDO, Hiroaki NAKAJIMA, Hirokazu KAMEOKA, "Independent Low-Rank Matrix Analysis Based on Generalized Kullback-Leibler Divergence" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 2, pp. 458-463, February 2019, doi: 10.1587/transfun.E102.A.458.
Abstract: In this letter, we propose a new blind source separation method, independent low-rank matrix analysis based on generalized Kullback-Leibler divergence. This method assumes a time-frequency-varying complex Poisson distribution as the source generative model, which yields convex optimization in the spectrogram estimation. The experimental evaluation confirms the proposed method's efficacy.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.458/_p
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@ARTICLE{e102-a_2_458,
author={Shinichi MOGAMI, Yoshiki MITSUI, Norihiro TAKAMUNE, Daichi KITAMURA, Hiroshi SARUWATARI, Yu TAKAHASHI, Kazunobu KONDO, Hiroaki NAKAJIMA, Hirokazu KAMEOKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Independent Low-Rank Matrix Analysis Based on Generalized Kullback-Leibler Divergence},
year={2019},
volume={E102-A},
number={2},
pages={458-463},
abstract={In this letter, we propose a new blind source separation method, independent low-rank matrix analysis based on generalized Kullback-Leibler divergence. This method assumes a time-frequency-varying complex Poisson distribution as the source generative model, which yields convex optimization in the spectrogram estimation. The experimental evaluation confirms the proposed method's efficacy.},
keywords={},
doi={10.1587/transfun.E102.A.458},
ISSN={1745-1337},
month={February},}
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TY - JOUR
TI - Independent Low-Rank Matrix Analysis Based on Generalized Kullback-Leibler Divergence
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 458
EP - 463
AU - Shinichi MOGAMI
AU - Yoshiki MITSUI
AU - Norihiro TAKAMUNE
AU - Daichi KITAMURA
AU - Hiroshi SARUWATARI
AU - Yu TAKAHASHI
AU - Kazunobu KONDO
AU - Hiroaki NAKAJIMA
AU - Hirokazu KAMEOKA
PY - 2019
DO - 10.1587/transfun.E102.A.458
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2019
AB - In this letter, we propose a new blind source separation method, independent low-rank matrix analysis based on generalized Kullback-Leibler divergence. This method assumes a time-frequency-varying complex Poisson distribution as the source generative model, which yields convex optimization in the spectrogram estimation. The experimental evaluation confirms the proposed method's efficacy.
ER -
English
