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

• Published in: Signal processing 2014-03, Vol.96, p.382-394
• Main Authors: Zhong, Xionghu, Hopgood, James R.
• Format: Article
• Language: English
• Subjects: Acoustic source tracking; Acoustic sources; Algorithms; Applied sciences; Audio signals; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Extended Kalman filter; Filtering; Filtration; Information, signal and communications theory; Particle filtering; Position (location); Room reverberation; Signal and communications theory; Signal, noise; Sound filters; Telecommunications and information theory; Time-delay of arrival; Tracking
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2013.09.002

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. •The problem of nonconcurrent multiple acoustic source tracking (NMAT) in a room (reverberant) environment is considered.•An extended Kalman particle filtering is introduced to approximate the optimal importance function.•Additional amplitude information inherent in the feature extraction stage is incorporated into the tracking algorithm.•Both simulated room environment and real lab experiments are organized to assess the advantage of the proposed approach.