Multi-label imbalanced classification poses a significant challenge in
machine learning, particularly evident in bioacoustics where animal sounds
often co-occur, and certain sounds are much less frequent than others. This
paper focuses on the specific case of classifying anuran species sounds using
the dataset AnuraSet, that contains both class imbalance and multi-label
examples. To address these challenges, we introduce Mixture of Mixups (Mix2), a
framework that leverages mixing regularization methods Mixup, Manifold Mixup,
and MultiMix. Experimental results show that these methods, individually, may
lead to suboptimal results; however, when applied randomly, with one selected
at each training iteration, they prove effective in addressing the mentioned
challenges, particularly for rare classes with few occurrences. Further
analysis reveals that Mix2 is also proficient in classifying sounds across
various levels of class co-occurrences.

通过引入 Mixture of Mixups（Mix2）框架，利用混合正则化方法 Mixup、Manifold Mixup 和 MultiMix，本文针对多标签不平衡分类的挑战，特别关注在包含类别不平衡和多标签示例的 AnuraSet 数据集中对无尾两栖物种声音进行分类。实验结果表明，这些方法单独使用可能导致次优结果，但在每次训练迭代中随机选择一个方法应用时，它们证明在解决上述挑战方面非常有效，特别适用于少数出现的稀有类别。进一步分析显示，Mix2 还能高效分类在不同类别共现水平的声音。