CBLDNN-Based Speaker-Independent Speech Separation Via Generative Adversarial Training

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Voice Separation with an Unknown Number of Multiple Speakers (2020, methodology): “We compare with the following baseline methods: ADANet (Luo et al., 2018), DPCL++ (Isik et al., 2016), CBLDNN-GAT (Li et al., 2018), TasNet (Luo & Mesgarani, 2018), the Ideal Ratio Mask (IRM), the Ideal Binary Mask (IBM), ConvTasNet (Luo & Mesgarani, 2019), FurcaNeXt (Zhang et al., 2020), and DPRNN ”
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation (2018, methodology): “The numbers of parameters in different methods are based on our implementations, except for [11], in which the network hyper-parameters are unspecified.”
Divide and Conquer: A Deep CASA Approach to Talker-Independent Monaural Speaker Separation (2019, background): “Recent uPIT improvements include new network structure [24], [43] and new training objectives [24].”
FurcaNeXt: End-to-end monaural speech separation with dynamic gated dilated temporal convolutional networks (2019, unknown): “8 CBLDNN-GAT [15] 11.”
Wavesplit: End-to-End Speech Separation by Speaker Clustering (2020, unknown): “(Luo et al., 2018) 10.4 10.8 Grid LSTM PIT (Xu et al., 2018) – 10.2 ConvLSTM-GAT (Li et al., 2018) – 11.0 Chimera++ (Wang et al., 2018b) 11.5 12.0 WA-MISI-5 (Wang et al., 2018c) 12.6 13.1 blstm-TasNet (Luo & Mesgarani, 2018) 13.2 13.6 Conv-TasNet (Luo & Mesgarani, 2019) 15.3 15.6 Conv-TasNet+MBT…”