让模型在不看旧数据的情况下，逐步学会不同环境下的10类声音分类。
Domain-Agnostic Incremental Learning for Sound Classification. A DCASE 2026 Challenge task
- 新任务学习时不接触旧数据，仅靠当前域信息进行增量训练。
- 基线系统在后两个域平均准确率仅52.5%，主因是测试时域识别错误。
- 适合研究音频领域适应与无数据回溯的持续学习方法者。
本文介绍了DCASE 2026挑战赛中的「领域无关增量学习音频分类任务」。增量学习指在不丢失已有知识的前提下，连续学习新任务。针对声音分类的领域增量学习，即在同一组声音类别下，于不同声学环境下逐步学习，首次被正式设为数据挑战赛。参赛者需训练一个系统，在三个不同声学领域中学习十类声音，且每次增量任务均无法访问先前任务的数据。提交系统将根据三个领域上的平均准确率排名。开发阶段提供的基线系统在后两个领域的平均准确率为52.5%，主要由于测试样本的领域误判导致性能下降。
原文摘要 · Abstract (English)
This paper presents the Domain-Agnostic Incremental Learning for Audio Classification Task of the DCASE 2026 Challenge. Incremental learning refers to sequentially learning new tasks with the same system while maintaining its knowledge and performance on the previously learned task. Domain-incremental learning for sound classification refers to learning the same sound classes but in different acoustic domains, and was formalized as a data challenge for the first time in DCASE 2026. Participants will train a system to learn ten sound classes in three different domains, with learning at each incremental task not having access to previous task data. Submitted systems will be ranked by the overall average accuracy calculated over the three domains. During the development stage, the provided baseline system obtains a modest performance of 52.5\% accuracy over the last two domains, mostly due to erroneous inference of the domain for the test sample.
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