7 citations · 13 across the 4 of their papers we have counts for
4 papers
Boosting Unknown-number Speaker Separation with Transformer Decoder-based Attractor
Younglo Lee, Shukjae Choi, Byeong-Yeol Kim +2
We propose a novel speech separation model designed to separate mixtures with an unknown number of speakers. The proposed model stacks 1) a dual-path processing block that can mode…
Neural Speech Enhancement with Very Low Algorithmic Latency and Complexity via Integrated Full- and Sub-Band Modeling
Zhong-Qiu Wang, Samuele Cornell, Shukjae Choi +3
We propose FSB-LSTM, a novel long short-term memory (LSTM) based architecture that integrates full- and sub-band (FSB) modeling, for single- and multi-channel speech enhancement in…
TF-GridNet: Integrating Full- and Sub-Band Modeling for Speech Separation
Zhong-Qiu Wang, Samuele Cornell, Shukjae Choi +3
We propose TF-GridNet for speech separation. The model is a novel deep neural network (DNN) integrating full- and sub-band modeling in the time-frequency (T-F) domain. It stacks se…
TF-GridNet: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation
Zhong-Qiu Wang, Samuele Cornell, Shukjae Choi +3
We propose TF-GridNet, a novel multi-path deep neural network (DNN) operating in the time-frequency (T-F) domain, for monaural talker-independent speaker separation in anechoic con…