  Separating an audio scene such as a cocktail party into constituent,
meaningful components is a core task in computer audition. Deep networks are
the state-of-the-art approach. They are trained on synthetic mixtures of audio
made from isolated sound source recordings so that ground truth for the
separation is known. However, the vast majority of available audio is not
isolated. The brain uses primitive cues that are independent of the
characteristics of any particular sound source to perform an initial
segmentation of the audio scene. We present a method for bootstrapping a deep
model for music source separation without ground truth by using multiple
primitive cues. We apply our method to train a network on a large set of
unlabeled music recordings from YouTube to separate vocals from accompaniment
without the need for ground truth isolated sources or artificial training
mixtures.
