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Simultaneous Tracking and Separation of Multiple Sources Using Factor Graph Model
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DOI：10.1109/TASLP.2020.3028650.png)
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摘要
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In this article, we present an algorithm for direction of arrival (DOA) tracking and separation of multiple speakers with a microphone array using the factor graph statistical model. In our model, the speakers can be located in one of a predefined set of candidate DOAs, and each time-frequency (TF) bin can be associated with a single speaker. Accordingly, by attributing a statistical model to both the DOAs and the associations, as well as to the microphone array observations given these variables, we show that the conditional probability of these variables given the microphone array observations can be modeled as a factor graph. Using the loopy belief propagation (LBP) algorithm, we derive a novel inference scheme which simultaneously estimates both the DOAs and the associations. These estimates are used in turn for separating the sources, by directing a beamformer towards the estimated DOAs, and then applying a TF masking according to the estimated associations. A comprehensive experimental study demonstrates the benefits of the proposed algorithm in both simulated data and real-life measurements recorded in our laboratory.
Keyword:
Direction-of-arrival estimation
Heuristic algorithms
Task analysis
Inference algorithms
Hidden Markov models
Microphone arrays
Speaker tracking
speaker separation
factor graphs
loopy belief propagation (LBP)
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