  We present a monophonic source separation system that is trained by only
observing mixtures with no ground truth separation information. We use a deep
clustering approach which trains on multi-channel mixtures and learns to
project spectrogram bins to source clusters that correlate with various spatial
features. We show that using such a training process we can obtain separation
performance that is as good as making use of ground truth separation
information. Once trained, this system is capable of performing sound
separation on monophonic inputs, despite having learned how to do so using
multi-channel recordings.
