Title :
Fast Bayesian acoustic localization
Author :
Birchfield, Stanley T. ; Gillmor, Daniel Kahn
Author_Institution :
Quindi Corporation, 480 S. California Ave., Palo Alto, 94306, USA
Abstract :
We derive a probabilistic formulation, based upon Bayes´ rule, for the acoustic localization problem. The resulting formula is shown to be closely related to the energy of a conventionally beamformed signal. We then present a close approximation to both which is much faster to compute — by two orders of magnitude with our experimental setup. The fast algorithm is essentially a generalization of approaches based upon time delay estimates (TDE´s), by applying the principle of least commitment. Experiments on real signals demonstrate accurate localization in noisy, reverberant environments (less than 3 dB SNR) several times faster than real time.
Keywords :
Array signal processing; Artificial intelligence; Filtering; Microphones; Signal to noise ratio;
Conference_Titel :
Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on
Print_ISBN :
0-7803-7402-9
DOI :
10.1109/ICASSP.2002.5744971