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Deep neural networks (DNNs) have shown promising results for acoustic echo cancellation (AEC).
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Z. Wang, Y. Na, Z. Liu, B. Tian, and Q. Fu, “Weighted recursive least square filter and neural network based residual echo suppression for the aec-challenge,” in ICASSP . IEEE, 2021, pp. 141–145
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2020
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K. Sridhar, R. Cutler, A. Saabas, T. Parnamaa, M. Loide, H. Gamper, S. Braun, R. Aichner, and S. Srinivasan, “ICASSP 2021 acoustic echo cancellation challenge: Datasets, testing framework, and results,” in ICASSP . IEEE, 2021, pp. 151–155
2021
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R. Cutler, A. Saabas, T. Parnamaa, M. Loide, S. Sootla, M. Purin, H. Gamper, S. Braun, K. Sorensen, R. Aichner, and S. Srinivasan, “INTERSPEECH 2021 acoustic echo cancellation challenge,” in INTERSPEECH , 2021, pp. 4748–4752
2021
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N. Westhausen and B. Meyer, “Acoustic echo cancellation with the dual-signal transformation LSTM network,” in ICASSP . IEEE, 2021, pp. 7138–7142
2021
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S. Zhang, Y. Kong, S. Lv, Y. Hu, and L. Xie, “F-T-LSTM based complex network for joint acoustic echo cancellation and speech enhancement,” in INTERSPEECH . ISCA, 2021, pp. 4758–4762
2021
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2021
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R. Cutler, A. Saabas, T. Parnamaa, M. Purin, H. Gamper, S. Braun, K. Sorensen, and R. Aichner, “ICASSP 2022 acoustic echo cancellation challenge,” in ICASSP . IEEE, 2022
2022
Closest in time.
S. Zhang, Z. Wang, J. Sun, Y. Fu, B. Tian, Q. Fu, and L. Xie, “Multi-task deep residual echo suppression with echo-aware loss,” in ICASSP . IEEE, 2022
2022
Closest in time.
S. E. Eskimez, T. Yoshioka, H. Wang, X. Wang, Z. Chen, and X. Huang, “Personalized speech enhancement: New models and comprehensive evaluation,” 2022
2022
Closest in time.
Y. Ju, W. Rao, X. Yan, Y. Fu, S. Lv, L. Cheng, Y. Wang, L. Xie, and S. Shang, “TEA-PSE: Tencent-Ethereal-Audio-Lab Personalized Speech Enhancement System for ICASSP 2022 DNS Challenge,” in ICASSP , 2022, pp. 9291–9295
2022
Closest in time.