On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Nonlinear filtering for speaker tracking in noisy and reverberant environments
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
EURASIP Journal on Applied Signal Processing
Particle filter with integrated voice activity detection for acoustic source tracking
EURASIP Journal on Applied Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Sound source localization using compressive sensing-based feature extraction and spatial sparsity
Digital Signal Processing
Directional acoustic source orientation estimation using only two microphones
Digital Signal Processing
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Particle Filter-based Acoustic Source Localization algorithms attempt to track the position of a sound source--one or more people speaking in a room--based on the current data from a microphone array as well as all previous data up to that point. This paper first discusses some of the inherent behavioral traits of the steered beamformer localization function. Using conclusions drawn from that study, a multitarget methodology for acoustic source tracking based on the Track Before Detect (TBD) framework is introduced. The algorithm also implicitly evaluates source activity using a variable appended to the state vector. Using the TBD methodology avoids the need to identify a set of source measurements and also allows for a vast increase in the number of particles used for a comparitive computational load which results in increased tracking stability in challenging recording environments. An evaluation of tracking performance is given using a set of real speech recordings with two simultaneously active speech sources.