Sparseness-based 2ch BSS using the em algorithm in reverberant environment

In this paper, we propose a new approach to sparseness-based BSS based on the EM algorithm, which iteratively estimates the DOA and the time-frequency mask for each source through the EM algorithm under the sparseness assumption. Our method has the following characteristics: 1) it enables the introduction of physical observation models such as the diffuse sound field, because the likelihood is defined in the original signal domain and not in the feature domain, 2) one does not necessarily have to know in advance the power of the background noise since they are also parameters which can be estimated from the observed signal, 3) it takes short computational time, 4) a common objective function is iteratively increased in localization and separation steps, which correspond to the E-step and M-step, respectively. Although our framework is applicable to general N channel BSS, we will concentrate on the formulation of the problem in the particular case where two sensory inputs are available, and we show some numerical simulation results.