Investigating catastrophic forgetting in sound event classification

2026-09-10

AI Summary

研究通过冻结特征提取器和微调动态头分类器的方法，解决了声音事件分类中灾难性遗忘的问题。

Abstract

This work investigates a number of approaches to prevent catastrophic forgetting in class incremental learning scenarios for sound event classification tasks. We analyze the problem using architectural and regularization approaches, using FSD50K and AudioSet datasets. We design incremental stages and solutions that selectively protect the kernels of the network from weight updates to prevent catastrophic forgetting, and a dynamic head solution that expands itself each time a new task is learned. The findings show that catastrophic forgetting mainly happens in deeper layers, in particular in the classifier head. For the studied in-domain sound classification problem, the solution that seems to alleviate catastrophic forgetting and is the most efficient is a full freezing of the feature extractor with a fine-tuning of the dynamic head classifier, showing little to no forgetting and great training stability, and a good balance between memory-stability and learning plasticity.

Problem

• catastrophic forgetting
• class incremental learning
• sound event classification

Innovation

• catastrophic forgetting
• incremental learning
• dynamic head
• feature extractor freezing
• sound event classification