Improved Expectation-Maximization Algorithm for Unknown Reverberant Audio-Source Separation

Abstract

The problem of undecided Separating reverberant audio sources is crucial for speech and audio processing. Numerous separation strategies have been developed to solve this problem; however, all of them estimate model parameters in the time–frequency domain, resulting in permutation ambiguity and poor separation performance. Additionally, one of the main challenges with existing expectation–maximization (EM) strategies is the time needed for each iterative step to update the model parameters. In this article, we offer an enhanced EM approach that combines nonnegative matrix factorization (NMF) with time differences of arrival (TDOA) estimations while eliminating time expenditure to the EM algorithm's starting values being appropriately selected. The suggested approach avoids permutation ambiguity by using the NMF source model, and acoustic localization is accomplished by converting the TDOA. Following that, model parameters are changed to improve separation outcomes. Finally, Wiener filters are used to separate the source signals. The experimental findings indicate that the suggested algorithm outperforms current blind separation approaches in terms of source separation.