新方法让声音定位模型不断学新声音，还保护隐私。
Analytic Class Incremental Learning for Sound Source Localization with Privacy Protection
- 用闭式解析解增量更新深度学习模型，避免遗忘旧知识。
- 在公开数据集上达到90.9%定位准确率，优于现有方法。
- 无需存储历史数据，适合智能家居等隐私敏感场景。
声源定位（SSL）技术广泛应用于监控和机器人领域。传统基于信号处理（SP）的方法在特定信号与噪声假设下可提供解析解，而近年基于深度学习（DL）的方法显著超越了它们。然而，其成功依赖大量训练数据和高算力，且通常需要大规模标注的空间数据，在适应新声音类别时表现不佳。为缓解上述挑战，我们提出一种新型类增量学习（CIL）方法——SSL-CIL，通过闭式解析解增量更新基于DL的SSL模型，有效避免灾难性遗忘。特别地，由于学习过程不回访任何历史数据（无实例存储），可保障数据隐私，更适用于智能家庭场景。在公开的SSLR数据集上的实证结果表明，本方法性能优越，定位准确率达90.9%，超过其他竞争方法。
原文摘要 · Abstract (English)
Sound Source Localization (SSL) enabling technology for applications such as surveillance and robotics. While traditional Signal Processing (SP)-based SSL methods provide analytic solutions under specific signal and noise assumptions, recent Deep Learning (DL)-based methods have significantly outperformed them. However, their success depends on extensive training data and substantial computational resources. Moreover, they often rely on large-scale annotated spatial data and may struggle when adapting to evolving sound classes. To mitigate these challenges, we propose a novel Class Incremental Learning (CIL) approach, termed SSL-CIL, which avoids serious accuracy degradation due to catastrophic forgetting by incrementally updating the DL-based SSL model through a closed-form analytic solution. In particular, data privacy is ensured since the learning process does not revisit any historical data (exemplar-free), which is more suitable for smart home scenarios. Empirical results in the public SSLR dataset demonstrate the superior performance of our proposal, achieving a localization accuracy of 90.9%, surpassing other competitive methods.
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