Ego noise reduction for hose-shaped rescue robot combining independent low-rank matrix analysis and multichannel noise cancellation

Narumi Mae, Masaru Ishimura, Shoji Makino, Daichi Kitamura, Nobutaka Ono, Takeshi Yamada, Hiroshi Saruwatari

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

In this paper, we present an ego noise reduction method for a hose-shaped rescue robot, developed for search and rescue operations in large-scale disasters. It is used to search for victims in disaster sites by capturing their voices with its microphone array. However, ego noises are mixed with voices, and it is difficult to differentiate them from a call for help from a disaster victim. To solve this problem, we here propose a two-step noise reduction method involving the following: (1) the estimation of both speech and ego noise signals from observed multichannel signals by multichannel nonnegative matrix factorization (NMF) with the rank-1 spatial constraint, and (2) the application of multichannel noise cancellation to the estimated speech signal using reference signals. Our evaluations show that this approach is effective for suppressing ego noise.

本文言語English
ホスト出版物のタイトルLatent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings
編集者Petr Tichavsky, Massoud Babaie-Zadeh, Olivier J.J. Michel, Nadege Thirion-Moreau
出版社Springer Verlag
ページ141-151
ページ数11
ISBN(印刷版)9783319535463
DOI
出版ステータスPublished - 2017
外部発表はい
イベント13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017 - Grenoble, France
継続期間: 2017 2 212017 2 23

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10169 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017
CountryFrance
CityGrenoble
Period17/2/2117/2/23

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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