An ICA-based RFS approach for DOA tracking of unknown time-varying numberof sources

Methods based on frequency-domain independent component analysis (ICA) in junction with state coherence transform (SCT) have been shown to be robust for extracting source location information like direction of Arrival (DOA) in highly reverberant environments and in the presence of spatial aliasing. Also, by exploiting the frequency sparsity of the sources, such methods have proven to be effective when the number of simultaneous sources is larger than the number of microphones. In many real world problems the number of concurrent speakers is unknown and varies with time as new speakers can appear and existing speakers can disappear or undergo silence periods. In order to deal with this challenging scenario of unknown time-varying number of speakers, we propose the use of the probability hypothesis density (PHD) filter which is based on random finite sets (RFS), where the multi-target states and the number of targets are integrated to form a set-valued variable. The tracking capabilities of the proposed method is demonstrated using simulations of multiple sources in reverberant environments.