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.

翻译：多标签不平衡分类是机器学习中的一个重大挑战，尤其在生物声学领域中尤为突出，因为动物声音常常同时出现，且某些声音的频率远低于其他声音。本文聚焦于使用AnuraSet数据集对蛙类物种声音进行分类的具体案例，该数据集同时存在类别不平衡和多标签样本问题。为应对这些挑战，我们提出了混合混合方法（Mix2），这是一个利用混合正则化方法Mixup、Manifold Mixup和MultiMix的框架。实验结果表明，这些方法单独使用时可能导致次优结果；然而，当随机应用，即在每次训练迭代中选择其中之一时，它们在应对上述挑战方面表现出色，尤其对出现次数较少的稀有类别效果显著。进一步分析显示，Mix2在分类不同类别共现程度的音频方面同样表现出色。