We present an algorithm to perform blind, one-microphone speech sep- aration. Instead, we formulate the problem of speech sep- aration as a problem in segmenting the spectrogram of the signal into two or more disjoint sets. We build feature sets for our segmenter using classical cues from speech psychophysics. We then combine these fea- tures into parameterized affinity matrices. We also take advantage of the fact that we can generate training examples for segmentation by artifi- cially superposing separately-recorded signals.

We present an algorithm to perform blind, one-microphone speech separation.

We present an algorithm to perform blind, one-microphone speech separation.

We present an algorithm to perform blind, one-microphone speech separation.