Multi-Hypothesis Square-Root Cubature Kalman Particle Filter for Speaker Tracking in Noisy and Reverberant Environments

Abstract

In this paper, a multi-hypothesis square-root cubature Kalman particle filter (MH-SRCKPF) is proposed for speaker tracking in noisy and reverberant environments with distributed microphone arrays. The conventional cubature Kalman particle filter (CKPF) uses the cubature Kalman filter (CKF) to generate its proposal for particle sampling. Such a proposal incorporates only one observation from a certain localization function for the state estimation, which is vulnerable to noise or reverberation, yielding the degraded tracking performance. To tackle the problem, by incorporating multiple possible observations into CKF for the proposal, a multi-hypothesis CKPF (MH-CKPF) algorithm is first developed. Furthermore, to improve the numerical stability, an MH-SRCKPF algorithm is developed, where the state estimate and the square root of the error covariance are propagated at each time. Finally, the MH-SRCKPF is applied to the speaker tracking problems in distributed microphone arrays. Experimental results demonstrate that the proposed MH-SRCKPF outperforms the competing methods in the presence of noise and reverberation. Meanwhile, by propagating the square root of the state covariance, the proposed method exhibits attractive numerical characteristics.