  In this paper, we formulate a blind source separation (BSS) framework, which
allows integrating U-Net based deep learning source separation network with
probabilistic spatial machine learning expectation maximization (EM) algorithm
for separating speech in reverberant conditions. Our proposed model uses a
pre-trained deep learning convolutional neural network, U-Net, for clustering
the interaural level difference (ILD) cues and machine learning expectation
maximization (EM) algorithm for clustering the interaural phase difference
(IPD) cues. The integrated model exploits the complementary strengths of the
two approaches to BSS: the strong modeling power of supervised neural networks
and the ease of unsupervised machine learning algorithms, whose few parameters
can be estimated on as little as a single segment of an audio mixture. The
results 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 based
source separation algorithm MESSL-GS (model-based expectation-maximization
source separation and localization with garbage source) and 4.5 dB in SDR and
8% in STOI over deep learning convolutional neural network (U-Net) based speech
separation algorithm SONET under the reverberant conditions ranging from
anechoic to those mostly encountered in the real world.
