Variational Inference for DOA Estimation in Reverberant Conditions

A concurrent speaker direction of arrival (DOA) estimator in a reverberant environment is presented. The reverberation phenomenon, if not properly addressed, is known to degrade the performance of DOA estimators. In this paper, we investigate a variational Bayesian (VB) inference framework for clustering time-frequency (TF) bins to candidate angles. The received microphone signals are modelled as a sum of anechoic speech and the reverberation component. Our model relies on Gaussian prior for the speech signal and Gamma prior for the speech precision. The noise covariance matrix is modelled by a time-invariant full-rank coherence matrix multiplied by time-varying gain with Gamma prior as well. The benefits of the presented model are verified in a simulation study using measured room impulse responses.