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Separating an audio scene into isolated sources is a fundamental problem incomputer audition, analogous to image segmentation in visual scene analysis.Source separation systems based on deep learning are currently the mostsuccessful approaches for solving the underdetermined separation problem, wherethere are more sources than channels. Traditionally, such systems are trainedon sound mixtures where the ground truth decomposition is already known. Sincemost real-world recordings do not have such a decomposition available, thislimits the range of mixtures one can train on, and the range of mixtures thelearned models may successfully separate. In this work, we use a simple blindspatial source separation algorithm to generate estimated decompositions ofstereo mixtures. These estimates, together with a weighting scheme in thetime-frequency domain, based on confidence in the separation quality, are usedto train a deep learning model that can be used for single-channel separation,where no source direction information is available. This demonstrates how asimple cue such as the direction of origin of source can be used to bootstrap amodel for source separation that can be used in situations where that cue isnot available.

Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures