Speaker Tracking on Multiple-Manifolds with Distributed Microphones
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Speaker tracking in a reverberant enclosure with an ad hoc network of multiple distributed microphones is addressed in this paper. A set of prerecorded measurements in the enclosure of interest is used to construct a data-driven statistical model. The function mapping the measurement-based features to the corresponding source position represents complex unknown relations, hence it is modelled as a random Gaussian process. The process is defined by a covariance function which encapsulates the relations among the available measurements and the different views presented by the distributed microphones. This model is intertwined with a Kalman filter to capture both the smoothness of the source movement in the time-domain and the smoothness with respect to patterns identified in the set of available prerecorded measurements. Simulation results demonstrate the ability of the proposed method to localize a moving source in reverberant conditions.
KeywordsSpeaker tracking Distributed microphones Gaussian process Acoustic manifold Kalman filter
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