A particlefiltering algorithm using the parameters in the EM (Expectation-Maximization) algorithm is proposed for tracking multiple sound sources. Differently from the conventional EM based algorithms, the proposed a...
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ISBN:
(纸本)9781424407286
A particlefiltering algorithm using the parameters in the EM (Expectation-Maximization) algorithm is proposed for tracking multiple sound sources. Differently from the conventional EM based algorithms, the proposed algorithm can track multiple sound sources without knowing their starting points. Moreover, an idea of the group tracking is applied to the particlefiltering algorithm so that better tracking performances can be obtained. Experimental results show the validity of the proposed algorithm.
The extension of particlefiltering techniques to the multiple speaker case is difficult as two distinct problems must now be addressed. Firstly, the active speakers must be identified and their locations estimated, r...
详细信息
ISBN:
(纸本)9781424414833
The extension of particlefiltering techniques to the multiple speaker case is difficult as two distinct problems must now be addressed. Firstly, the active speakers must be identified and their locations estimated, requiring the use of multi-dimensional likelihoods, and then each speaker must be correctly associated with his corresponding location. In this paper we propose a multi-speaker tracking algorithm in which the number of active speakers is determined by estimating the profile of the noise-plus-reverberation covariance matrix eigen-values. The multi-dimensional likelihoods are then decoupled using the Expectation Maximization (EM) algorithm. The tracking accuracy is improved by the inclusion of a pause detection step and estimation of the noise-plus-interference covariance matrix. The results show the benefits of the proposed methods under difficult tracking situations.
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