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
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
相关论文
共 50 条
  • [31] Semi-supervised SRL System with Bayesian Inference
    Lorenzo, Alejandra
    Cerisara, Christophe
    [J]. COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, CICLING 2014, PT I, 2014, 8403 : 429 - 441
  • [32] Semi-supervised learning for Bayesian pattern classification
    Center, JL
    [J]. Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 2005, 803 : 517 - 524
  • [33] Incremental semi-supervised graph learning NMF with block-diagonal
    Lv, Xue
    Leng, Chengcai
    Peng, Jinye
    Pei, Zhao
    Cheng, Irene
    Basu, Anup
    [J]. Engineering Applications of Artificial Intelligence, 2024, 130
  • [34] Semi-supervised underwater acoustic source localization based on residual convolutional autoencoder
    Jin, Pian
    Wang, Biao
    Li, Lebo
    Chao, Peng
    Xie, Fangtong
    [J]. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2022, 2022 (01)
  • [35] Semi-supervised underwater acoustic source localization based on residual convolutional autoencoder
    Pian Jin
    Biao Wang
    Lebo Li
    Peng Chao
    Fangtong Xie
    [J]. EURASIP Journal on Advances in Signal Processing, 2022
  • [36] Incremental semi-supervised graph learning NMF with block-diagonal
    Lv, Xue
    Leng, Chengcai
    Peng, Jinye
    Pei, Zhao
    Cheng, Irene
    Basu, Anup
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 130
  • [37] Semi-Supervised NMF in the chroma Domain Applied to Music Harmony Estimation
    Takahashi, Takuya
    Hori, Takeshi
    Wilk, Christoph M.
    Sagayama, Shigeki
    [J]. 2018 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC), 2018, : 1636 - 1641
  • [38] SNFM: A semi-supervised NMF algorithm for detecting biological functional modules
    Man, Yutong
    Liu, Guangming
    Yang, Kuo
    Zhou, Xuezhong
    [J]. MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2019, 16 (04) : 1933 - 1948
  • [39] Semi-supervised NMF with Local and Global Label Embedding for Data Representation
    Qian, Bin
    Gu, Xiguang
    Liu, Fan
    Tong, Lei
    [J]. PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019), 2019, : 440 - 444
  • [40] Semi-supervised dimensional sentiment analysis with variational autoencoder
    Wu, Chuhan
    Wu, Fangzhao
    Wu, Sixing
    Yuan, Zhigang
    Liu, Junxin
    Huang, Yongfeng
    [J]. KNOWLEDGE-BASED SYSTEMS, 2019, 165 : 30 - 39