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Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality.
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2017
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S.-W. Fu, T.-W. Wang, Y. Tsao, X. Lu, and H. Kawai, “End-to-end waveform utterance enhancement for direct evaluation metrics optimization by fully convolutional neural networks,”
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2018
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2018
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2018
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2018
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2019
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2020
Closest in time.
L. Zhang, Z. Shi, J. Han, A. Shi, and D. Ma, “Furcanext: End-to-end monaural speech separation with dynamic gated dilated temporal convolutional networks,” in
2020
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Y. Xia, S. Braun, C. K. A. Reddy, H. Dubey, R. Cutler, and I. Tashev, “Weighted speech distortion losses for neural-network-based real-time speech enhancement,” in
2020
Closest in time.