Multi-Source Estimation Consistency for Improved Multiple Direction-of-Arrival Estimation

S. Hafezi, A. H. Moore, and P. A. Naylor (Imperial College London)
Workshop on on Hands-free Speech Communication and Microphone Arrays (HSCMA), San Francisco, USA, March 1-3, 2017
[showhide type=”Abstract”] Abstract:In Direction-of-Arrival(DOA) estimation for multiple sources, removal of noisy data points from a set of local DOA estimates increases the resulting estimation accuracy, especially when there are many sources and they have small angular separation. In this work, we propose a post-processing technique for the enhancement of DOA extraction from a set of local estimates using the consistency of these estimates within the time frame based on adaptive multi-source assumption. Simulations in a realistic reverberant environment with sensor noise and up to 5 sources demonstrate that the proposed technique outperforms the baseline and state-of-the-art approaches. In these tests the proposed technique had the worst average error of 9◦, robustness of 5◦ to widely varying source separation and 3◦ to number of sources. [/showhide]