  Human auditory cortex excels at selectively suppressing background noise to
focus on a target speaker. The process of selective attention in the brain is
known to contextually exploit the available audio and visual cues to better
focus on target speaker while filtering out other noises. In this study, we
propose a novel deep neural network (DNN) based audiovisual (AV) mask
estimation model. The proposed AV mask estimation model contextually integrates
the temporal dynamics of both audio and noise-immune visual features for
improved mask estimation and speech separation. For optimal AV features
extraction and ideal binary mask (IBM) estimation, a hybrid DNN architecture is
exploited to leverages the complementary strengths of a stacked long short term
memory (LSTM) and convolution LSTM network. The comparative simulation results
in terms of speech quality and intelligibility demonstrate significant
performance improvement of our proposed AV mask estimation model as compared to
audio-only and visual-only mask estimation approaches for both speaker
dependent and independent scenarios.
