AudioCIL工具箱让音频模型持续学习新声音类别，不遗忘旧知识。
AudioCIL: A Python Toolbox for Audio Class-Incremental Learning with Multiple Scenes
- 构建可增量学习新音频类别的开源工具箱
- 支持多场景下持续添加新声音类别
- 适合研究动态音频环境的模型开发
深度学习在音频信号处理中表现出色，依赖静态大规模数据集训练，但现实世界中音频类别不断变化，新声音持续出现或因隐私限制临时存在。为应对这一动态性，需模型能持续学习新类别而不遗忘旧知识。本文提出AudioCIL工具箱，推动音频分类任务向真实场景演进，支持音频类增量学习（AuCIL）。该工具箱提供统一框架，使算法能适应多场景下的持续学习需求，代码已开源：https://github.com/colaudiolab/AudioCIL。
原文摘要 · Abstract (English)
Deep learning, with its robust aotomatic feature extraction capabilities, has demonstrated significant success in audio signal processing. Typically, these methods rely on static, pre-collected large-scale datasets for training, performing well on a fixed number of classes. However, the real world is characterized by constant change, with new audio classes emerging from streaming or temporary availability due to privacy. This dynamic nature of audio environments necessitates models that can incrementally learn new knowledge for new classes without discarding existing information. Introducing incremental learning to the field of audio signal processing, i.e., Audio Class-Incremental Learning (AuCIL), is a meaningful endeavor. We propose such a toolbox named AudioCIL to align audio signal processing algorithms with real-world scenarios and strengthen research in audio class-incremental learning. Code is available at https://github.com/colaudiolab/AudioCIL.
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