Echo avoidance in a computational model of the precedence effect
Speech Communication
Target tracking by time difference of arrival using recursive smoothing
Signal Processing
Robotics and Autonomous Systems
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
Time delay estimation in room acoustic environments: an overview
EURASIP Journal on Applied Signal Processing
Particle filter with integrated voice activity detection for acoustic source tracking
EURASIP Journal on Applied Signal Processing
Implementation and calibration of a Bayesian binaural system for 3D localisation
ROBIO '09 Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics
Evaluating real-time audio localization algorithms for artificial audition in robotics
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Intelligent acoustic rotor speed estimation for an autonomous helicopter
Applied Soft Computing
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This paper deals with the problem of localizing and tracking a moving speaker over the full range around the mobile robot. The problem is solved by taking advantage of the phase shift between signals received at spatially separated microphones. The proposed algorithm is based on estimating the time difference of arrival by maximizing the weighted cross-correlation function in order to determine the azimuth angle of the detected speaker. The cross-correlation is enhanced with an adaptive signal-to-noise estimation algorithm to make the azimuth estimation more robust in noisy surroundings. A post-processing technique is proposed in which each of these microphone-pair determined azimuths are further combined into a mixture of von Mises distributions, thus producing a practical probabilistic representation of the microphone array measurement. It is shown that this distribution is inherently multimodal and that the system at hand is non-linear. Therefore, particle filtering is applied for discrete representation of the distribution function. Furthermore, the two most common microphone array geometries are analysed and exhaustive experiments were conducted in order to qualitatively and quantitatively test the algorithm and compare the two geometries. Also, a voice activity detection algorithm based on the before-mentioned signal-to-noise estimator was implemented and incorporated into the existing speaker localization system. The results show that the algorithm can reliably and accurately localize and track a moving speaker.