Single Channel Speech Separation Using Source-Filter Representation

We propose a fully probabilistic model for source-filter based single channel source separation. In particular, we perform separation in a sequential manner, where we estimate the source-driven aspects by a factorial HMM used for multi-pitch estimation. Afterwards, these pitch tracks are combined with the vocal tract filter model to form an utterance dependent model. Additionally, we introduce a gain estimation approach to enable adaptation to arbitrary mixing levels in the speech mixtures. We thoroughly evaluate this system and finally end up in a speaker independent model.