  We propose DiffSep, a new single channel source separation method based on
score-matching of a stochastic differential equation (SDE). We craft a tailored
continuous time diffusion-mixing process starting from the separated sources
and converging to a Gaussian distribution centered on their mixture. This
formulation lets us apply the machinery of score-based generative modelling.
First, we train a neural network to approximate the score function of the
marginal probabilities or the diffusion-mixing process. Then, we use it to
solve the reverse time SDE that progressively separates the sources starting
from their mixture. We propose a modified training strategy to handle model
mismatch and source permutation ambiguity. Experiments on the WSJ0 2mix dataset
demonstrate the potential of the method. Furthermore, the method is also
suitable for speech enhancement and shows performance competitive with prior
work on the VoiceBank-DEMAND dataset.
