Cubature Kalman-Probability Hypothesis Density Filter for Multiple Speakers Tracking

Aiming at nonlinear system model in multiple speakers tracking, a cubature Kalman-probability hypothesis density filter for multiple speakers tracking is proposed in this paper. Time difference of arrival for microphone array is taken as measurements, third-degree spherical-radial rule is utilized to compute the multidimensional integral in Bayesian filter of nonlinear system in proposed method, cubature Kalman filter and probability hypothesis density filter is applied to estimate first-order statistical moment of posterior multiple speakers states, and finally multiple speakers tracking of nonlinear Gaussian system is realized while the speakers’ states are extracted by recursive updating. Compared with some filters in multiple speakers tracking, the proposed method has several advantages. Calculating Jacobian matrix of nonlinear system function, which is usually hard to be done, is no longer necessary in proposed filter and computational complexity also goes down. Simulation experiments have been taken to analyze the performance of proposed method when detection probability, false speakers’ number, sampling period, speech-signal-to-noise ratio and reverberation time varies. Simulation results show that the proposed method reduces the impact on the performance of filtering algorithm from nonlinear system model, enhances the robustness of the algorithm, and improves estimation accuracy of multiple speakers’ number and states.