Particle filtering for TDOA based acoustic source tracking: Nonconcurrent Multiple Talkers

摘要

Room reverberation introduces multipath components into an audio signal and causes problems for acoustic source localization and tracking. Existing tracking methods based on the extended Kalman filter (EKF) and sequential importance resampling based particle filter (SIR-PF) usually assume that a single source is constantly active in the tracking scene. Assuming that multiple talkers may appear alternatively during a conversation, this paper develops an extended Kalman particle filtering (EKPF) approach for nonconcurrent multiple acoustic tracking (NMAT). Essentially, an EKF is introduced to obtain an optimum importance sampling, by which the particles are drawn according to the current time-delay of arrival (TDOA) measurements as well as the previous position estimates. Hence, the proposed approach can quickly adapt to the sharp position change when the source switches and the tracking lag in SIR-PF can be avoided. Moreover, the amplitude of the TDOA measurement is investigated to formulate a measurement hypothesis prior. Such a prior is fused into the tracking algorithm to enhance the tracking accuracy. Both simulations and real audio lab experiments are organized to study the tracking performance. The results demonstrate that the proposed EKPF approaches outperforms the SIR-PF and EKF in a broad range of tracking scenarios.