Sound source localization; identity tracking are fundamental tasks in acoustic scene analysis, enabling machines to determine what, where; when produces sound events. While deep attractor-based networks have demonstrated improved performance under an unknown number of sources, maintaining continuous source tracking over long-form audio remains challenging due to memory limitations; permutation ambiguities across adjacent segments. In this paper, we propose a Recursive Attractor Network (RANet) for long-form sound source localization; identity tracking with a variable number of sources. RANet explicitly represents source attractors as transferable embeddings; recursively propagates them across adjacent audio segments using a LSTM-based model, thereby preserving source identity continuity over time. Experimental results on simulated datasets demonstrate that RANet achieves robust long-form sound source localization; consistent source identity tracking, outperforming baseline approaches under variable; dynamic source conditions.
