Title: Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures,
Abstract: Separating an audio scene into isolated sources is a fundamental problem in
computer audition, analogous to image segmentation in visual scene analysis.
Source separation systems based on deep learning are currently the most
successful approaches for solving the underdetermined separation problem, where
there are more sources than channels. Traditionally, such systems are trained
on sound mixtures where the ground truth decomposition is already known. Since
most real-world recordings do not have such a decomposition available, this
limits the range of mixtures one can train on, and the range of mixtures the
learned models may successfully separate. In this work, we use a simple blind
spatial source separation algorithm to generate estimated decompositions of
stereo mixtures. These estimates, together with a weighting scheme in the
time-frequency domain, based on confidence in the separation quality, are used
to train a deep learning model that can be used for single-channel separation,
where no source direction information is available. This demonstrates how a
simple cue such as the direction of origin of source can be used to bootstrap a
model for source separation that can be used in situations where that cue is
not available.