Comparison of Semi-supervised Deep Learning Algorithms for Audio Classification - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue EURASIP Journal on Audio, Speech, and Music Processing Année : 2022

Comparison of Semi-supervised Deep Learning Algorithms for Audio Classification

Résumé

In this article, we adapted five recent SSL methods to the task of audio classification. The first two methods, namely Deep Co-Training (DCT) and Mean Teacher (MT), involve two collaborative neural networks. The three other algorithms, called MixMatch (MM), ReMixMatch (RMM), and FixMatch (FM), are single-model methods that rely primarily on data augmentation strategies. Using the Wide-ResNet-28-2 architecture in all our experiments, 10% of labeled data and the remaining 90% as unlabeled data for training, we first compare the error rates of the five methods on three standard benchmark audio datasets: Environmental Sound Classification (ESC-10), UrbanSound8K (UBS8K), and Google Speech Commands (GSC). In all but one cases, MM, RMM, and FM outperformed MT and DCT significantly, MM and RMM being the best methods in most experiments. On UBS8K and GSC, MM achieved 18.02% and 3.25% error rate (ER) respectively, outperforming models trained with 100% of the available labeled data, which reached 23.29% and 4.94%, respectively. RMM achieved the best results on ESC-10 (12.00% ER), followed by FM which reached 13.33%. Second, we explored adding the mixup augmentation, used in MM and RMM, to DCT, MT and FM. In almost all cases, mixup brought consistent gains. For instance, on GSC, FM reached 4.44% and 3.31% ER without and with mixup. Our PyTorch code will be made available upon paper acceptance at https://github.com/Labbeti/SSLH.
Comparison_of_Semi_supervised_Deep_Learning_Algorithms_for_Audio_Classification.pdf (656.98 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03855042 , version 1 (16-11-2022)

Licence

Paternité

Identifiants

Citer

Léo Cances, Etienne Labbé, Thomas Pellegrini. Comparison of Semi-supervised Deep Learning Algorithms for Audio Classification. EURASIP Journal on Audio, Speech, and Music Processing, 2022, 2022 (1, article 23), pp.1-14. ⟨10.1186/s13636-022-00255-6⟩. ⟨hal-03855042⟩
31 Consultations
76 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More