Improvement of Spatial Ambiguity in Multi-Channel Speech Separation Using Channel Attention

Qian Bei Hong, Chung Hsien Wu, Thanh Binh Nguyen, Hsin Min Wang

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Multi-channel speech separation has been successfully applied in a complex real-world environment such as the far-field condition. The common solution to deal with the far-field condition is using a multi-channel signal captured by a structured microphone array and leveraging the inner difference between channels to enhance the speech separation performance. The spatial feature has been widely used in recent speech separation research. This feature appears to be insufficient when the location information becomes ambiguous. This is known as the spatial ambiguity problem. In order to deal with the spatial ambiguity problem, this study proposes an attention mechanism for the Temporal-Spatial Neural Filter (TSNF), in which the channel attention on merged features and the feature map of 1D convolution block in the temporal convolution network is proposed. The proposed method is evaluated on the multi-channel reverberant dataset which is built based on the WSJ0-2mix dataset. The dataset is simulated in the real-environment room by using the Room Impulse Response generator. In the experimental results, the proposed methods produced the SI -SNR improvement of about 1.2dB in close speakers' case, while a small decrease of 0.1dB in other cases.

原文English
主出版物標題2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面619-623
頁數5
ISBN(電子)9789881476890
出版狀態Published - 2021
事件2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan
持續時間: 2021 12月 142021 12月 17

出版系列

名字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
國家/地區Japan
城市Tokyo
期間21-12-1421-12-17

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦視覺和模式識別
  • 訊號處理
  • 儀器

指紋

深入研究「Improvement of Spatial Ambiguity in Multi-Channel Speech Separation Using Channel Attention」主題。共同形成了獨特的指紋。

引用此