Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911929264504832 |
|---|---|
| author | Moummad, Ilyass Farrugia, Nicolas Serizel, Romain Froidevaux, Jeremy Lostanlen, Vincent |
| author_facet | Moummad, Ilyass Farrugia, Nicolas Serizel, Romain Froidevaux, Jeremy Lostanlen, Vincent |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09598 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds Moummad, Ilyass Farrugia, Nicolas Serizel, Romain Froidevaux, Jeremy Lostanlen, Vincent Sound Machine Learning Audio and Speech Processing 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. |
| title | Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2403.09598 |