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.