Particle filter (PF) has been proved to be an effective tool to track sound sources. In traditional PF, a pre-defined dynamic model is used to model source motion, which tends to be mismatched due to the uncertainty of source motion. Besides, non-stationary interferences pose a severe challenge to source tracking. To this end, an observation-guided adaptive particle filter (OAPF) is proposed for multiple concurrent sound source tracking. Firstly, sensor signals are processed in the time-frequency domain to obtain the direction of arrival (DOA) observations of sources. Then, by updating particle states with these DOA observations, angular distances between particles and observations are reduced to guide particles to directions of sources. Thirdly, particle weights are updated by an interference-adaptive likelihood function to reduce the impacts of interferences. At last, with the updated particles and the corresponding weights, OAPF is utilized to determine the final DOAs of sources. Experimental results demonstrate that our method achieves favorable performance for multiple concurrent sound source tracking in noisy environments.
Cite as: Liu, H., Lan, H., Yang, B., Pang, C. (2018) Multiple Concurrent Sound Source Tracking Based on Observation-Guided Adaptive Particle Filter. Proc. Interspeech 2018, 826-830, doi: 10.21437/Interspeech.2018-1248
@inproceedings{liu18c_interspeech,
author={Hong Liu and Haipeng Lan and Bing Yang and Cheng Pang},
title={{Multiple Concurrent Sound Source Tracking Based on Observation-Guided Adaptive Particle Filter}},
year=2018,
booktitle={Proc. Interspeech 2018},
pages={826--830},
doi={10.21437/Interspeech.2018-1248}
}