We propose a new approach for clustering competing speech sources using distributed microphone arrays. In this approach, we first define two feature vectors where the first captures the intra-node location information...
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ISBN:
(纸本)9781479903566
We propose a new approach for clustering competing speech sources using distributed microphone arrays. In this approach, we first define two feature vectors where the first captures the intra-node location information while the second captures the level difference of speech energy recorded at different nodes. Then, we introduce Watson and Dirichlet mixture models to model the first and second features, respectively. We integrate both types of information in an expectation maximization algorithm to cluster the simultaneous speech sources. The performance of the proposed approach is superior to best node selection and comparable to centralized processing in terms of conventional blind source separation metrics.
distributed microphone array (DMA) processing has recently been gathering increasing research interest due to its various applications and diverse challenges. In many conventional multichannel speech enhancement algor...
详细信息
ISBN:
(纸本)9781629934433
distributed microphone array (DMA) processing has recently been gathering increasing research interest due to its various applications and diverse challenges. In many conventional multichannel speech enhancement algorithms that use co-located microphones, such as the multi-channel Wiener filtering and mask-based blind source separation (BSS) approaches, statistics of the target and interference signals are required if we are to design an optimal enhancement filter. To obtain such statistics, we estimate activity information regarding source and interference signals (hereafter, source activity information), that is generally assumed to be common to all the microphones. However, in DMA scenarios, the source activities observable at any given microphone may be significantly different from those of others when the microphones are spatially distributed to a great degree, and the level of each signal at each microphone varies significantly. Thus, to capture such source activity information appropriately and thereby achieve optimal speech enhancement in DMA environments, in this paper we propose an approach for estimating microphone-dependent source activity, and for performing blind source separation based on such information. The proposed method estimates the activity of each source signal at each microphone, which can be explained by the microphone independent speech log power spectra and microphone-location dependent source gains. We introduce a probabilistic formulation of the proposed method, and an efficient algorithm for model parameter estimation. We show the efficacy of the proposed method experimentally in comparison with conventional methods in various DMA scenarios.
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