Broadband DOA Estimation Using Learning-Based Optimal Statistics Estimates

For accurate direction of arrival (DOA) estimation in challenging high reverberation and low signal-to-noise ratio (SNR) scenarios, various deep learning (DL) techniques have been developed to incorporate and enhance existing algorithms. However, addressing the challenges of enhancing network manipulability, constructing efficient learning features, and integrating algorithms in a rational manner remains a set of significant hurdles. In this article, we use DL to obtain the speech presence probability (SPP) to construct the optimal statistics estimates, which are then combined with traditional algorithms to achieve accurate DOA estimation. Specifically, we explore the application of the a posteriori SPP in DOA estimation, design a reverberation separation model for practical scenarios, and derive and validate a new computational equation for SPP under this model. Besides, we propose a frequency bin-wise network structure to improve network fitting efficiency and construct input features accordingly. Moreover, by adopting a combined structure, we avoid full-angle network feature training and instead train on partial angles under deliberate subset classification. We then evaluate the DOA estimation performance for the entire direction range with fine resolution using this approach. Simulation results demonstrate that the proposed method requires smaller datasets compared to end-to-end DL algorithms. Furthermore, the results validate that the proposed method outperforms both DL-based end-to-end approaches and traditional full-band approaches in terms of accuracy and error rate across various reverberation and SNR conditions.