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The probability hypothesis density (PHD) filter based on sequential Monte Carlo (SMC) approximation (also known as SMC-PHD filter) has proven to be a promising algorithm for multispeaker tracking. However, it has a heavy computational cost as surviving, spawned, and born particles need to be distributed in each frame to model the state of the speakers and to estimate jointly the variable number of...
Sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has received much interest in the field of nonlinear non-Gaussian visual tracking due to its ability to handle a variable number of speakers. The SMC-PHD filter employs surviving, spawned and born particles to model the state of the speakers and jointly estimates the variable number of speakers with their states. The born particles...
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