  Separation of competing speech is a key challenge in signal processing and a
feat routinely performed by the human auditory brain. A long standing benchmark
of the spectrogram approach to source separation is known as the ideal binary
mask. Here, we train a convolutional deep neural network, on a two-speaker
cocktail party problem, to make probabilistic predictions about binary masks.
Our results approach ideal binary mask performance, illustrating that
relatively simple deep neural networks are capable of robust binary mask
prediction. We also illustrate the trade-off between prediction statistics and
separation quality.
