  Non-zero diffusion particle flow Sequential Monte Carlo probability
hypothesis density (NPF-SMC-PHD) filtering has been recently introduced for
multi-speaker tracking. However, the NPF does not consider the missing
detection which plays a key role in estimation of the number of speakers with
their states. To address this limitation, we propose to use intensity particle
flow (IPF) in NPFSMC-PHD filter. The proposed method, IPF-SMC-PHD, considers
the clutter intensity and detection probability while no data association
algorithms are used for the calculation of particle flow. Experiments on the
LOCATA (acoustic source Localization and Tracking) dataset with the sequences
of task 4 show that our proposed IPF-SMC-PHD filter improves the tracking
performance in terms of estimation accuracy as compared to its baseline
counterparts.
