Deep Speech Extraction with Time-Varying Spatial Filtering Guided by Desired Direction Attractor

Yu Nakagome, Masahito Togami, Tetsuji Ogawa, Tetsunori Kobayashi

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

In this investigation, a deep neural network (DNN) based speech extraction method is proposed to enhance a speech signal propagating from the desired direction. The proposed method integrates knowledge based on a sound propagation model and the time-varying characteristics of a speech source, into a DNN-based separation framework. This approach outputs a separated speech source using time-varying spatial filtering, which achieves superior speech extraction performance compared with time-invariant spatial filtering. Given that the gradient of all modules can be calculated, back-propagation can be performed to maximize the speech quality of the output signal in an end-to-end manner. Guided information is also modeled based on the sound propagation model, which facilitates disentangled representations of the target speech source and noise signals. The experimental results demonstrate that the proposed method can extract the target speech source more accurately than conventional DNN-based speech source separation and conventional speech extraction using time-invariant spatial filtering.

本文言語English
ホスト出版物のタイトル2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ671-675
ページ数5
ISBN(電子版)9781509066315
DOI
出版ステータスPublished - 2020 5
イベント2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
継続期間: 2020 5 42020 5 8

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2020-May
ISSN(印刷版)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period20/5/420/5/8

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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