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arXiv · 2601.18335

Analytic Incremental Learning For Sound Source Localization With Imbalance Rectification

Zexia Fan·Yu Chen·Qiquan Zhang·Kainan Chen·Xinyuan Qian
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Abstract

Sound source localization (SSL) demonstrates remarkable results in controlled settings but struggles in real-world deployment due to dual imbalance challenges: intra-task imbalance arising from long-tailed direction-of-arrival (DoA) distributions, and inter-task imbalance induced by cross-task skews and overlaps. These often lead to catastrophic forgetting, significantly degrading the localization accuracy. To mitigate these issues, we propose a unified framework with two key innovations. Specifically, we design a GCC-PHAT-based data augmentation (GDA) method that leverages peak characteristics to alleviate intra-task distribution skews. We also propose an Analytic dynamic imbalance rectifier (ADIR) with task-adaption regularization, which enables analytic updates that adapt to inter-task dynamics. On the SSLR benchmark, our proposal achieves state-of-the-art (SoTA) results of 89.0% accuracy, 5.3{\deg} mean absolute error, and 1.6 backward transfer, demonstrating robustness to evolving imbalances without exemplar storage.

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Zexia Fan, Yu Chen, Qiquan Zhang, Kainan Chen, Xinyuan Qian. 2026-01-26. Analytic Incremental Learning For Sound Source Localization With Imbalance Rectification. https://arxiv.org/abs/2601.18335

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