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.

翻译：深度学习凭借其强大的自动特征提取能力，在音频信号处理领域已展现出显著成效。通常，这些方法依赖于静态、预先收集的大规模数据集进行训练，在固定数量的类别上表现良好。然而，现实世界处于持续变化之中，新的音频类别可能因数据流式输入或隐私保护导致的临时可用性而不断出现。音频环境的这种动态特性要求模型能够在不丢弃已有知识的前提下，持续学习新类别的新知识。将增量学习引入音频信号处理领域，即音频类增量学习，是一项具有重要意义的工作。为此，我们提出了名为AudioCIL的工具箱，旨在使音频信号处理算法更贴合实际应用场景，并推动音频类增量学习的研究发展。