提出动态网络架构，解决音频分类中跨场景学习时的遗忘问题。
Domain-Incremental Learning for Audio Classification
- 设计可增量学习的动态网络，保留共性特征并适应新领域
- 跨域分类平均准确率达71.9%~83.4%，多标签lwlrap达40.7%~47.5%
- 适用于声学场景与音频数据集的持续学习，适合长期部署系统
本文提出一种面向音频分类的领域增量学习方法，针对在不同声学条件下连续获取数据集时模型易遗忘旧知识、完全微调又难以适应新领域的问题。我们设计了一种新型动态网络架构，既能保留各领域间的同质声学特征，又能逐步学习领域特异性特征，在保持历史知识与适应新领域之间取得平衡。实验在欧洲城市与韩国的声学场景单标签分类任务，以及Audioset与FSD50K的多标签音频分类任务上验证了该方法的有效性。在欧洲城市→韩国和韩国→欧洲城市的顺序下，单标签分类平均准确率分别为71.9%和83.4%；在Audioset→FSD50K与FSD50K→Audioset的多标签设置中，平均lwlrap分别为47.5%和40.7%。
原文摘要 · Abstract (English)
In this work, we propose a method for domain-incremental learning for audio classification from a sequence of datasets recorded in different acoustic conditions. Fine-tuning a model on a sequence of evolving domains or datasets leads to forgetting of previously learned knowledge. On the other hand, freezing all the layers of the model leads to the model not adapting to the new domain. In this work, our novel dynamic network architecture keeps the shared homogeneous acoustic characteristics of domains, and learns the domain-specific acoustic characteristics in incremental steps. Our approach achieves a good balance between retaining the knowledge of previously learned domains and acquiring the knowledge of the new domain. We demonstrate the effectiveness of the proposed method on incremental learning of single-label classification of acoustic scenes from European cities and Korea, and multi-label classification of audio recordings from Audioset and FSD50K datasets. The proposed approach learns to classify acoustic scenes incrementally with an average accuracy of 71.9% for the order: European cities -> Korea, and 83.4% for Korea -> European cities. In a multi-label audio classification setup, it achieves an average lwlrap of 47.5% for Audioset -> FSD50K and 40.7% for FSD50K -> Audioset.
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