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• W4200634403 abstract "In recent years, a number of time-domain speech separation methods have been proposed. However, most of them are very sensitive to the environments and wide domain coverage tasks. In this paper, from the time-frequency domain perspective, we propose a densely-connected pyramid complex convolutional network, termed DPCCN, to improve the robustness of speech separation under complicated conditions. Furthermore, we generalize the DPCCN to target speech extraction (TSE) by integrating a new specially designed speaker encoder. Moreover, we also investigate the robustness of DPCCN to unsupervised cross-domain TSE tasks. A Mixture-Remix approach is proposed to adapt the target domain acoustic characteristics for fine-tuning the source model. We evaluate the proposed methods not only under noisy and reverberant in-domain condition, but also in clean but cross-domain conditions. Results show that for both speech separation and extraction, the DPCCN-based systems achieve significantly better performance and robustness than the currently dominating time-domain methods, especially for the cross-domain tasks. Particularly, we find that the Mixture-Remix fine-tuning with DPCCN significantly outperforms the TD-SpeakerBeam for unsupervised cross-domain TSE, with around 3.5 dB SISNR improvement on target domain test set, without any source domain performance degradation." @default.
• W4200634403 created "2021-12-31" @default.
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• W4200634403 date "2022-05-23" @default.
• W4200634403 modified "2023-09-26" @default.
• W4200634403 title "DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation and Extraction" @default.
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• W4200634403 doi "https://doi.org/10.1109/icassp43922.2022.9747340" @default.
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