Close Sound Source Localization incorporating Semi-Supervised Variational Bayesian NMF

被引：0
|
作者：
Kumon, Makoto [1 ]
Washizaki, Kai [2 ]
Nakadai, Kazuhiro [3 ,4 ]
机构：
[1] Kumamoto Univ, Int Res Org Adv Sci, Fac Adv Sci & Technol, Chuo Ku, 2-39-1 Kurokami, Kumamoto 8608555, Japan
[2] Kumamoto Univ, Grad Sch Sci & Technol, Chuo Ku, 2-39-1 Kurokami, Kumamoto 8608555, Japan
[3] Tokyo Inst Technol, Meguro Ku, 2-12-1 Ookayama, Tokyo 1528552, Japan
[4] Honda Res Inst Japan, Meguro Ku, 2-12-1 Ookayama, Tokyo 1528552, Japan
来源：
2019 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION (SII) | 2019年
关键词：
ENHANCEMENT;
D O I：
10.1109/sii.2019.8700459
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper proposes a method to integrate a sound source separation method based on Nonnegative Matrix Factorization approach that is able to cope with partially known acoustic characteristics, and a sound source localization method with a frequency bin selection utilizing the separated signals. The proposed method is effective in the case when the target sound source locates close to a noise source, and the noise characteristics are available owing to the sound sound separation suppress the effect of noise. Numerical simulations showed that the proposed system succeeded to localize two sources of 5 degrees interval, which validates the approach.
引用
收藏
页码：313 / 318
页数：6
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