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In this paper, we formulate a blind source separation (BSS) framework, whichallows integrating U-Net based deep learning source separation network withprobabilistic spatial machine learning expectation maximization (EM) algorithmfor separating speech in reverberant conditions. Our proposed model uses apre-trained deep learning convolutional neural network, U-Net, for clusteringthe interaural level difference (ILD) cues and machine learning expectationmaximization (EM) algorithm for clustering the interaural phase difference(IPD) cues. The integrated model exploits the complementary strengths of thetwo approaches to BSS: the strong modeling power of supervised neural networksand the ease of unsupervised machine learning algorithms, whose few parameterscan be estimated on as little as a single segment of an audio mixture. Theresults show an average improvement of 4.3 dB in signal to distortion ratio(SDR) and 4.3% in short time speech intelligibility (STOI) over the EM basedsource separation algorithm MESSL-GS (model-based expectation-maximizationsource separation and localization with garbage source) and 4.5 dB in SDR and8% in STOI over deep learning convolutional neural network (U-Net) based speechseparation algorithm SONET under the reverberant conditions ranging fromanechoic to those mostly encountered in the real world.

Integration of deep learning with expectation maximization for spatial cue based speech separation in reverberant conditions