  This paper will describe a novel approach to the cocktail party problem that
relies on a fully convolutional neural network (FCN) architecture. The FCN
takes noisy audio data as input and performs nonlinear, filtering operations to
produce clean audio data of the target speech at the output. Our method learns
a model for one specific speaker, and is then able to extract that speakers
voice from babble background noise. Results from experimentation indicate the
ability to generalize to new speakers and robustness to new noise environments
of varying signal-to-noise ratios. A potential application of this method would
be for use in hearing aids. A pre-trained model could be quickly fine tuned for
an individuals family members and close friends, and deployed onto a hearing
aid to assist listeners in noisy environments.
