[64] H. Sawada, S. Araki, and S. Makino, "A Two-Stage Frequency-Domain Blind Source Separation Method for Underdetermined Convolutive Mixtures," in Proc. WASPAA, 2007, pp. 139-142.
[65] H. Sawada, R. Mukai, S. Araki, and S. Makino, “A Robust and Precise Method for Solving The Permutation Problem of Frequency-Domain Blind Source Separation,” IEEE Transactions on Speech and Audio Processing, vol. 12, no. 5, pp. 530–538, 2004.
[66] S. Gannot, E. Vincent, S. Markovich-Golan, and A. Ozerov, “A Consolidated Perspective on Multi-Microphone Speech Enhancement and Source Separation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, pp. 692–730, 2017.
[67] L. Drude, J. Heitkaemper, C. Boeddeker, and R. Haeb-Umbach, "SMS-WSJ: Database, Performance Measures, and Baseline Recipe for Multi-Channel Source Separation and Recognition," in arXiv preprint arXiv:1910.13934, 2019.
[68] C. Boeddeker, “https://github.com/fgnt/pb_bss/blob/master/examples/mixture_model_example.ipynb,” 2019.
[69] R. Scheibler and K. Saijo, “https://github.com/fakufaku/torchiva,” 2022.
[70] E. Vincent, R. Gribonval, and C. Févotte, "Performance Measurement in Blind Audio Source Separation," IEEE Transactions Audio, Speech, and Language Processing, vol. 14, no. 4, pp. 1462-1469, 2006.
[71] J. Le Roux, S. Wisdom, H. Erdogan, and J. R. Hershey, “SDR - Half-Baked or Well Done?” in Proc. ICASSP, 2019, pp. 626-630.
[72] A. Rix, J. Beerends, M. Hollier, and A. Hekstra, “Perceptual Evaluation of Speech Quality (PESQ)-A New Method for Speech Quality Assessment of Telephone Networks and Codes,” in Proc. ICASSP, vol. 2, 2001, pp. 749–752.
[73] C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen, "An Algorithm for Intelligibility Prediction of Time-Frequency Weighted Noisy Speech," IEEE Transactions Audio, Speech, and Language Processing, vol. 19, no. 7, pp. 2125-2136, 2011.
[74] Z.-Q. Wang, G. Wichern, and J. Le Roux, “Leveraging Low-Distortion Target Estimates for Improved Speech Enhancement,” arXiv preprint arXiv:2110.00570, 2021.
[75] Y. Luo, Z. Chen, and T. Yoshioka, "Dual-Path RNN: Efficient Long Sequence Modeling for Time-Domain Single-Channel Speech Separation," in Proc. ICASSP, 2020, pp. 46-50.
[76] S. Watanabe, M. Mandel, J. Barker, E. Vincent, A. Arora et al., "CHiME-6 Challenge: Tackling Multi-speaker Speech Recognition for Unsegmented Recordings," in arXiv preprint arXiv:2004.09249, 2020.
[77] J. Carletta, S. Ashby, S. Bourban, M. Flynn et al., “The AMI Meeting Corpus: A Pre-Announcement,” in International Workshop on Machine Learning for Multimodal Interaction, 2006, pp. 28–39.
[78] F. Yu, S. Zhang, Y. Fu, L. Xie, S. Zheng, Z. Du, W. Huang, P. Guo, Z. Yan, B. Ma, X. Xu, and H. Bu, "M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge," in Proc. ICASSP, 2022, pp. 6167-6171.
[79] K. Tan and D. Wang, "Learning Complex Spectral Mapping With Gated Convolutional Recurrent Networks for Monaural Speech Enhancement," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 380-390, 2020.
[80] Y. Hu, Y. Liu, S. Lv et al., "DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement," in Proc. Interspeech, 2020, pp. 2472-2476.
[81] Y. Liu and D. Wang, "Causal Deep CASA for Monaural Talker-Independent Speaker Separation," IEEE/ACM Transactions on Audio, Speech, and Language Processing, pp. 1270–1279, 2020.
[82] A. Pandey and D. Wang, “Densely Connected Neural Network with Dilated Convolutions for Real-Time Speech Enhancement in The Time Domain,” in Proc. ICASSP, 2020, pp. 6629–6633.
[83] H. Taherian, K. Tan, and D. Wang, "Multi-Channel Talker-Independent Speaker Separation Through Location-Based Training," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 2791-2800, 2022.
[84] J. Zhang, C. Zorila, R. Doddipatla, and J. Barker, "On End-to-End Multi-Channel Time Domain Speech Separation in Reverberant Environments," in Proc. ICASSP, 2020, pp. 6389-6393.