Improving Source Tracking Accuracy Through Learning-Based Estimation Methods in SH Domain: A Comparative Study

Acoustic source tracking is significant across applications like surveillance, teleconferencing, and robot audition, yet the complexity introduced by reverberation, background noise, and overlapping sources impedes precise source localization. This article uses learning-based localization methods to introduce a resilient and intelligent acoustic source tracking approach in the spherical harmonics (SHs) domain. The tracking algorithms anticipate moving source locations by leveraging past predictions and direction of arrival (DOA) estimations. The prediction probability is computed through alpha–beta and Kalman filtering applied to the estimated DOAs, which are likelihood probabilities obtained from learning models. Utilizing the spatial attributes of sound sources encoded in SH signals, diverse learning-based frameworks are introduced to capture the intricate relationship between SH features and source locations. Supervised learning is utilized to train the models that minimize localization errors between predicted and ground truth positions. Experimental assessments underscore the efficacy and resilience of our proposed approach, conducted using LOCAlization and TrAcking (LOCATA) data, revealing a substantial enhancement in tracking accuracy compared to baseline methods.