Joint DOA Estimation and Dereverberation Based on Multi-Channel Linear Prediction Filtering and Azimuth Sparsity

Source localization in reverberant environments has been a prominent research topic in the past two decades. In this paper, instead of the commonly employed time-frequency (TF) bin based methods which rely on empirically selected threshold values, we leverage the microphone array signal model comprising an early reverberant component and a late reverberant component, to propose a novel method for the source localization problem in reverberant environments. Our proposed criterion involves the joint removal of the late reverberant component using the multi-channel linear prediction (MCLP) filter, while estimating the directions of arrival (DOAs) of the actual sources using the early component signals. By applying the azimuth sparsity constraint, the true DOA can be estimated with high resolution and free from the interference of the early reflections. To solve the proposed criterion, DOAs, source signals, and MCLP filter coefficients are estimated by alternative iterations. Additionally, we present a source localization criterion specifically designed for the single source scenario as a special case of the multiple sources scenario. Finally, a source number estimation method and a postprocessing procedure are discussed for searching the global solutions to our proposed criteria. Evaluations with both simulated and realistic data demonstrate the advantages of our proposed methods over the baseline methods.