Comparison of Srp-phat and Multiband-popi Algorithms for Speaker Localization Using Particle Filters

The task of localizing single and multiple concurrent speakers in a reverberant environment with background noise poses several problems. One of the major problems is the severe corruption of the frame-wise localization estimates. To improve the overall localization accuracy, we propose a particle filter based tracking algorithm using the recently proposed Multiband Joint PositionPitch (M-PoPi) localization algorithm as a frame wise likelihood estimate. To prove the performance of our approach, we tested it on real-world recordings of seven different speakers and of up to three concurrent speakers. We compared our new approach to the well-known SRP-PHAT algorithm as frame-wise likelihood estimates. Finally, we compared both particle filter based tracking algorithms with their frame-wise localization algorithms. The MPoPi based particle filter tracking algorithm outperforms the SRPPHAT based particle filter tracking algorithm. The comparison with their frame wise localization algorithms shows that this improved performance stems from the more robust M-PoPi frame wise localization estimate.