Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds
Ilyass Moummad Nicolas Farrugia Romain Serizel Jeremy Froidevaux Vincent Lostanlen
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.