Central Difference Kalman-probability Hypothesis Density Filter for Multiple Speakers Tracking

Aiming at nonlinear system model in multiple speakers tracking, a central difference Kalman-probability hypothesis density filter for multiple speakers tracking is proposed in this paper. Time difference of arrival for microphone array is taken as measurement, Stirling interpolation formula is utilized to derive polynomial approximations of nonlinear functions, central difference Kalman filter and Gaussian mixture probability hypothesis density filter are 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. Simulation results show that the robustness of the algorithm is enhanced, and estimation accuracy of multiple speakers number and states is improved.

[1]  A. Doucet,et al.  Sequential Monte Carlo methods for multitarget filtering with random finite sets , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Michael Shapiro Brandstein,et al.  A framework for speech source localization using sensor arrays , 1995 .

[3]  Ba-Ngu Vo,et al.  A Consistent Metric for Performance Evaluation of Multi-Object Filters , 2008, IEEE Transactions on Signal Processing.

[4]  Michael R. M. Jenkin,et al.  Audiovisual localization of multiple speakers in a video teleconferencing setting , 2003, Int. J. Imaging Syst. Technol..

[5]  X. R. Li,et al.  Survey of maneuvering target tracking. Part I. Dynamic models , 2003 .

[6]  Yin Jianjun,et al.  Gaussian Sum PHD Filtering Algorithm for Nonlinear Non-Gaussian Models , 2008 .

[7]  Huimin Chen,et al.  Tracking of multiple moving speakers with multiple microphone arrays , 2004, IEEE Transactions on Speech and Audio Processing.

[8]  Ba-Ngu Vo,et al.  Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach , 2006, IEEE Transactions on Signal Processing.

[9]  Hou Dai-wen.  Central difference Kalman-probability hypothesis density filter for multitarget tracking , 2013 .

[10]  Jont B. Allen,et al.  Image method for efficiently simulating small‐room acoustics , 1976 .

[11]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[12]  F. B. Hildebrand,et al.  Introduction To Numerical Analysis , 1957 .

[13]  Niels Kjølstad Poulsen,et al.  New developments in state estimation for nonlinear systems , 2000, Autom..

[14]  Andrew Blake,et al.  Nonlinear filtering for speaker tracking in noisy and reverberant environments , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[15]  Jean-Marc Odobez,et al.  Audiovisual Probabilistic Tracking of Multiple Speakers in Meetings , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  Parham Aarabi,et al.  Acoustic robot navigation using distributed microphone arrays , 2004, Inf. Fusion.

[17]  Mohan M. Trivedi,et al.  Source localization in reverberant environments: modeling and statistical analysis , 2003, IEEE Trans. Speech Audio Process..

[18]  J. Stoer,et al.  Introduction to Numerical Analysis , 2002 .

[19]  Ba-Ngu Vo,et al.  The Gaussian Mixture Probability Hypothesis Density Filter , 2006, IEEE Transactions on Signal Processing.

[20]  S. Haykin,et al.  Cubature Kalman Filters , 2009, IEEE Transactions on Automatic Control.