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• W3163217847 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." @default.
• W3163217847 created "2021-05-24" @default.
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• W3163217847 date "2021-06-06" @default.
• W3163217847 modified "2023-09-30" @default.
• W3163217847 title "Convolutive Transfer Function Invariant SDR Training Criteria for Multi-Channel Reverberant Speech Separation" @default.
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• W3163217847 doi "https://doi.org/10.1109/icassp39728.2021.9414661" @default.
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