Low Latency Online Source Separation and Noise Reduction Based on Joint Optimization with Dereverberation

Tetsuya Ueda*, Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita, Shoko Araki, Shoji Makino

*この研究の対応する著者

研究成果: Conference contribution

抄録

This paper proposes low latency online source separation in noisy environments. An approach based on weighted prediction error dereverberation was recently proposed to solve the degradation caused by using low latency online source separation. Although this approach can also reduce noise by increasing the number of microphones and separating the noise as additional sources, the calculation cost prohibitively increases. To solve this problem, this paper incorporates techniques used in independent vector extraction (IVE) into the above conventional approach. Because IVE can skip most of the calculations for estimating noise by assuming that it is a stationary Gaussian, our proposed method achieves effective and computationally efficient noise reduction using many microphones. Experiments in a noisy car environment show that our proposed online method simultaneously separates sources and reduces noise with low latency (< 12 ms) processing.

本文言語English
ホスト出版物のタイトル29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
出版社European Signal Processing Conference, EUSIPCO
ページ1000-1004
ページ数5
ISBN(電子版)9789082797060
DOI
出版ステータスPublished - 2021
外部発表はい
イベント29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
継続期間: 2021 8月 232021 8月 27

出版物シリーズ

名前European Signal Processing Conference
2021-August
ISSN(印刷版)2219-5491

Conference

Conference29th European Signal Processing Conference, EUSIPCO 2021
国/地域Ireland
CityDublin
Period21/8/2321/8/27

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学

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