A Pitch-aware Approach to Single-channel Speech Separation

Ke Wang, Frank Soong, Lei Xie

科研成果: 书/报告/会议事项章节会议稿件同行评审

22 引用 (Scopus)

摘要

Despite significant advancements of deep learning on separating speech sources mixed in a single channel, same gender speaker mix, i.e., male-male or female-female, is still more difficult to separate than the case of opposite gender mix. In this study, we propose a pitch-aware speech separation approach to improve the speech separation performance. The proposed approach performs speech separation in the following steps: 1) training a pre-separation model to separate the mixed sources; 2) training a pitch-tracking network to perform polyphonic pitch tracking; 3) incorporating the estimated pitch for the final pitch-aware speech separation. Experimental results of the new approach, tested on the WSJ0-2mix public dataset, show that the new approach improves speech separation performance for both same and opposite gender mixture. The improved performance in signal-to-distortion (SDR) of 12.0 dB is the best reported result without using any phase enhancement.

源语言英语
主期刊名2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
296-300
页数5
ISBN(电子版)9781479981311
DOI
出版状态已出版 - 5月 2019
活动44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, 英国
期限: 12 5月 201917 5月 2019

出版系列

姓名ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2019-May
ISSN(印刷版)1520-6149

会议

会议44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
国家/地区英国
Brighton
时期12/05/1917/05/19

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