Abstract / truncated to 115 words (read the full abstract)

Single channel source separation is a quite recent problem of constantly growing interest in the scientific world. However, this problem is still very far to be solved, and even more, it cannot be solved in all its generality. Indeed, since this problem is highly underdetermined, the main difficulty is that a very strong knowledge about the sources is required to be able to separate them. For a grand class of existing separation methods, this knowledge is expressed by statistical source models, notably Gaussian Mixture Models (GMM), which are learned from some training examples. The subject of this work is to study the separation methods based on statistical models in general, and then to apply them ... toggle 8 keywords

single channel source separation – probabilistic models – bayesian adaptation – maximum a posteriori – bayesian networks – expectation maximization – gaussian mixture models – adaptive wiener filtering

Follow @eurasip