  Identification and extraction of singing voice from within musical mixtures
is a key challenge in source separation and machine audition. Recently, deep
neural networks (DNN) have been used to estimate 'ideal' binary masks for
carefully controlled cocktail party speech separation problems. However, it is
not yet known whether these methods are capable of generalizing to the
discrimination of voice and non-voice in the context of musical mixtures. Here,
we trained a convolutional DNN (of around a billion parameters) to provide
probabilistic estimates of the ideal binary mask for separation of vocal sounds
from real-world musical mixtures. We contrast our DNN results with more
traditional linear methods. Our approach may be useful for automatic removal of
vocal sounds from musical mixtures for 'karaoke' type applications.
