Lin Zhou1, *, Siyuan Lu1, Qiuyue Zhong1, Ying Chen1, 2, Yibin Tang3, Yan Zhou3
CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1373-1386, 2020, DOI:10.32604/cmc.2020.010182
Abstract Speaker separation in complex acoustic environment is one of challenging
tasks in speech separation. In practice, speakers are very often unmoving or moving
slowly in normal communication. In this case, the spatial features among the consecutive
speech frames become highly correlated such that it is helpful for speaker separation by
providing additional spatial information. To fully exploit this information, we design a
separation system on Recurrent Neural Network (RNN) with long short-term memory
(LSTM) which effectively learns the temporal dynamics of spatial features. In detail, a
LSTM-based speaker separation algorithm is proposed to extract the spatial features in
each time-frequency… More >
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