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Deep dilated temporal convolutional networks (TCN) have been proved to bevery effective in sequence modeling. In this paper we propose severalimprovements of TCN for end-to-end approach to monaural speech separation,which consists of 1) multi-scale dynamic weighted gated dilated convolutionalpyramids network (FurcaPy), 2) gated TCN with intra-parallel convolutionalcomponents (FurcaPa), 3) weight-shared multi-scale gated TCN (FurcaSh), 4)dilated TCN with gated difference-convolutional component (FurcaSu), that allthese networks take the mixed utterance of two speakers and maps it to twoseparated utterances, where each utterance contains only one speaker's voice.For the objective, we propose to train the network by directly optimizingutterance level signal-to-distortion ratio (SDR) in a permutation invarianttraining (PIT) style. Our experiments on the the public WSJ0-2mix data corpusresults in 18.4dB SDR improvement, which shows our proposed networks can leadsto performance improvement on the speaker separation task.

FurcaNeXt: End-to-end monaural speech separation with dynamic gated dilated temporal convolutional networks