Analytic Incremental Learning For Sound Source Localization With Imbalance Rectification

• URL: http://arxiv.org/abs/2601.18335v1
• Date: Mon, 26 Jan 2026 10:22:01 GMT
• Title: Analytic Incremental Learning For Sound Source Localization With Imbalance Rectification
• Authors: Zexia Fan, Yu Chen, Qiquan Zhang, Kainan Chen, Xinyuan Qian,
• Abstract summary: Sound source localization (SSL) demonstrates remarkable results in controlled settings but struggles in real-world deployment.<n>We propose a unified framework with two key innovations to mitigate these issues.<n>On the SSLR benchmark, our proposal achieves state-of-the-art (SoTA) results of 89.0% accuracy, 5.3 mean absolute error, and 1.6 backward transfer robustness.
• Score: 15.161051314343105
• License: http://creativecommons.org/licenses/by/4.0/
• 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° mean absolute error, and 1.6 backward transfer, demonstrating robustness to evolving imbalances without exemplar storage.

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