CONF Oualil_IWAENC_2012/IDIAP A Multiple Hypothesis Gaussian Mixture Filter for Acoustic Source Localization and Tracking Oualil, Youssef Faubel, Friedrich Klakow, Dietrich Oualil, Youssef Ed. EXTERNAL https://publications.idiap.ch/attachments/papers/2012/Oualil_IWAENC_2012.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/Oualil_Idiap-RR-09-2012 Related documents 13th International Workshop on Acoustic Signal Enhancement 2012 233-236 In this work, we address the problem of tracking an acoustic source based on measured time differences of arrival (TDOA). The classical solution to this problem consists in using a detector, which estimates the TDOA for each microphone pair, and then applying a tracking algorithm, which integrates the “measured” TDOAs in time. Such a two-stage approach presumes 1) that TDOAs can be estimated reliably; and 2) that the errors in detection behave in a well-defined fashion. The presence of noise and reverberation, however, causes larger errors in the TDOA estimates and, thereby, ultimately lowers the tracking performance. We propose to counteract this effect by considering a multiple hypothesis filter, which propagates the TDOA estimation uncertainty to the tracking stage. That is achieved by considering multiple TDOA estimates and then integrating the resulting TDOA observations in the framework of a Gaussian mixture filter. Experimental results show that the proposed filter has a significantly lower angular error than a multiple hypothesis particle filter. REPORT Oualil_Idiap-RR-09-2012/IDIAP A Multiple Hypothesis Gaussian Mixture Filter for Acoustic Source Localization and Tracking Oualil, Youssef Faubel, Friedrich Klakow, Dietrich Direction of arrival estimation Kalman filters microphone arrays Monte Carlo methods EXTERNAL https://publications.idiap.ch/attachments/reports/2012/Oualil_Idiap-RR-09-2012.pdf PUBLIC Idiap-RR-09-2012 2012 Idiap March 2012 Submitted to IEEE SSP Workshop 2012 In this work, we address the problem of tracking an acoustic source based on measured time difference of arrivals (TDOAs). The classical solution to this problem consists in using a detector, which estimates the TDOA for each microphone pair, and then applying a tracking algorithm, which integrates the "measured" TDOAs in time. Such a two-stage approach presumes (1) that TDOAs can be estimated reliably; and (2) that the errors in detection behave in a well-defined fashion. The presence of noise and reverberation, however, causes larger errors in the TDOA estimates and, thereby, ultimately lowers the tracking performance. We propose to counteract this effect by propagating the detection uncertainty. That is achieved by sampling from the GCCs and then integrating the resulting TDOAs in the framework of a Gaussian mixture filter. Experimental results show that the proposed filter has a significantly lower angular error than a multiple hypothesis particle filter.