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Found 5 papers, 5 papers with code
Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds
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.
Self-Supervised Learning for Few-Shot Bird Sound Classification
Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cost.
Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection
Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio.
Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning
The bioacoustic community recasted the problem of sound event detection within the framework of few-shot learning, i. e. training a system with only few labeled examples.
Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning
In addition, when combining class labels with metadata using multiple supervised contrastive learning, an extension of supervised contrastive learning solving an additional task of grouping patients within the same sex and age group, more informative features are learned.
