Developing new machine learning applications often requires the collection of new datasets. However, existing datasets may already contain relevant information to train models for new purposes. We propose SoundCollage: a framework to discover new classes within audio datasets by incorporating (1) an audio pre-processing pipeline to decompose different sounds in audio samples and (2) an automated model-based annotation mechanism to identify the discovered classes. Furthermore, we introduce clarity measure to assess the coherence of the discovered classes for better training new downstream applications. Our evaluations show that the accuracy of downstream audio classifiers within discovered class samples and held-out datasets improves over the baseline by up to 34.7% and 4.5%, respectively, highlighting the potential of SoundCollage in making datasets reusable by labeling with newly discovered classes. To encourage further research in this area, we open-source our code at https://github.com/nokia-bell-labs/audio-class-discovery.

翻译：开发新的机器学习应用通常需要收集新的数据集。然而，现有数据集可能已包含可用于训练新目标模型的相关信息。我们提出SoundCollage框架，通过整合（1）用于分解音频样本中不同声音的音频预处理流程，以及（2）基于模型的自动标注机制来识别所发现的类别，从而在音频数据集中发现新类别。此外，我们引入清晰度度量来评估所发现类别的内聚性，以更好地训练新的下游应用。评估结果表明，在发现的类别样本和留出数据集中，下游音频分类器的准确率较基线分别提升最高达34.7%和4.5%，这凸显了SoundCollage通过新发现类别进行标注来实现数据集复用的潜力。为促进该领域的进一步研究，我们在https://github.com/nokia-bell-labs/audio-class-discovery开源了代码。