WIENER BASED SOURCE SEPARATION WITH HMM/GMM USING A SINGLE SENSOR
Laurent Benaroya · 2003
We propose a new method to perform the separation of two audio sources from a single sensor. This method generalizes the Wiener filtering with Gaussian Mixture distributions and with Hidden Markov Models. The method involves a training phase of the models parameters, which is done with the classical EM algorithm. We derive a new algorithm for the re-estimation of the sources with these mixture models, during the separation phase. The general approach is evaluated on the separation of real audio data and compared to classical Wiener filtering.