Comparative Study on DNN-based Minimum Variance Beamforming Robust to Small Movements of Sound Sources

Kohei Saijo, Kazuhiro Katagiri, Masaru Fujieda, Tetsunori Kobayashi, Tetsuji Ogawa

研究成果: Conference contribution

抄録

This paper discusses a deep neural network (DNN)-based minimum variance (MV) beamformer suitable for the case where the target sound source moves slightly in front of the microphones. In practical applications of speech enhancement, such as a guidance terminal installed in a train station, the target sound source can be assumed to be located approximately in front of the microphones, although it may move slightly. Speech enhancement techniques used under such conditions can be classified into two types: one is to enhance the sound source while adaptively estimating its location, and the other is to enhance the area in front of the microphone array. The former requires localization of the target source but has a high degree of freedom of the beamformer, which can lead to high noise suppression performance, while the latter does not require the source localization but has a low degree of freedom of the beamformer. Speech enhancement experiments conducted to compare the performance of these approaches demonstrated that the MV beamformer based on adaptive sound source localization can provide more accurate enhancement than that based on area enhancement even when the sound source is moving.

本文言語English
ホスト出版物のタイトル2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ603-607
ページ数5
ISBN(電子版)9789881476890
出版ステータスPublished - 2021
イベント2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan
継続期間: 2021 12月 142021 12月 17

出版物シリーズ

名前2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings

Conference

Conference2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
国/地域Japan
CityTokyo
Period21/12/1421/12/17

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理
  • 器械工学

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