A Particle Filter Algorithm Based on Multi-feature Compound Model for Sound Source Tracking in Reverberant and Noisy Environments

Accurate measurement is an important prerequisite for sound source localization. In the enclosed environments, noise and reverberation tend to cause localization errors. To address these issues, this paper proposes a compound model particle filter algorithm based on multi-feature. Based on a multi-feature observation, the likelihood function of speaker tracking is constructed for particle filter, and multi-hypothesis and frequency selection function are adopted to establish multi-feature optimization mechanism, including time delay selection and beam output energy fusion. It is found that they effectively solved the difficulty in the simultaneous suppression of noise and reverberation by single feature. Moreover, considering the randomness of speaker motion, a compound model for sound source tracking is developed, where the stability of the speaker tracking system is improved by integrating multi-feature observation into the compound model filtering. The experimental results with both simulated and real acoustic data indicate that the proposed method has better tracking performance, compared with the existing ones with low SNR and strong reverberation as well as highly mobile conditions.