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Boosting Unknown-number Speaker Separation with Transformer Decoder-based Attractor
We propose a novel speech separation model designed to separate mixtures with an unknown number of speakers.
Ranked #1 on
Speech Separation
on WSJ0-5mix
Neural Speech Enhancement with Very Low Algorithmic Latency and Complexity via Integrated Full- and Sub-Band Modeling
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 the short-time Fourier transform (STFT) domain.
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