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Deep gated convolutional networks have been proved to be very effective insingle channel speech separation. However current state-of-the-art frameworkoften considers training the gated convolutional networks in time-frequency(TF) domain. Such an approach will result in limited perceptual score, such assignal-to-distortion ratio (SDR) upper bound of separated utterances and alsofail to exploit an end-to-end framework. In this paper we present an integratedsimple and effective end-to-end approach to monaural speech separation, whichconsists of deep gated convolutional neural networks (GCNN) that takes themixed utterance of two speakers and maps it to two separated utterances, whereeach utterance contains only one speaker's voice. In addition long short-termmemory (LSTM) is employed for long term temporal modeling. For the objective,we propose to train the network by directly optimizing utterance level SDR in apermutation invariant training (PIT) style. Our experiments on the publicWSJ0-2mix data corpus demonstrate that this new scheme can produce morediscriminative separated utterances and leading to performance improvement onthe speaker separation task.

FurcaNet: An end-to-end deep gated convolutional, long short-term memory, deep neural networks for single channel speech separation