2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSS-13.3
Paper Title CONVOLUTIVE TRANSFER FUNCTION INVARIANT SDR TRAINING CRITERIA FOR MULTI-CHANNEL REVERBERANT SPEECH SEPARATION
Authors Christoph Boeddeker, Paderborn University, Germany; Wangyou Zhang, Shanghai Jiao Tong University, China; Tomohiro Nakatani, Keisuke Kinoshita, Tsubasa Ochiai, Marc Delcroix, Naoyuki Kamo, NTT Corporation, Japan; Yanmin Qian, Shanghai Jiao Tong University, China; Reinhold Haeb-Umbach, Paderborn University, Germany
SessionSS-13: Recent Advances in Multichannel and Multimodal Machine Learning for Speech Applications
LocationGather.Town
Session Time:Thursday, 10 June, 16:30 - 17:15
Presentation Time:Thursday, 10 June, 16:30 - 17:15
Presentation Poster
Topic Special Sessions: Recent Advances in Multichannel and Multimodal Machine Learning for Speech Applications
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Time-domain training criteria have proven to be very effective for the separation of single-channel non-reverberant speech mixtures. Likewise, mask-based beamforming has shown impressive performance in multi-channel reverberant speech enhancement and source separation. Here, we propose to combine neural network supported multi-channel source separation with a time-domain training objective function. For the objective we propose to use a convolutive transfer function invariant Signal-to-Distortion Ratio (CI-SDR) based loss. While this is a well-known evaluation metric (BSS Eval), it has not been used as a training objective before. To show the effectiveness, we demonstrate the performance on LibriSpeech based reverberant mixtures. On this task, the proposed system approaches the error rate obtained on single-source non-reverberant input, i.e., LibriSpeech test_clean, with a difference of only 1.2 percentage points, thus outperforming a conventional permutation invariant training based system and alternative objectives like Scale Invariant Signal-to-Distortion Ratio by a large margin.