Deep metric learning for bioacoustic classification: Overcoming training data scarcity using dynamic triplet loss.

Area of Science:

• Bioacoustics
• Machine Learning
• Computer Science

Background:

• Bioacoustic classification faces challenges due to insufficient labeled data, limiting the application of advanced deep learning models.
• Existing methods struggle in data-scarce environments, hindering accurate species identification and ecological monitoring.

Purpose of the Study:

• To develop a deep metric learning framework for effective bioacoustic classification with limited per-class training examples.
• To introduce a dynamic variant of triplet loss to enhance class separation in a learned transformation space.
• To enable open-set classification capabilities, a feature lacking in current bioacoustic classification methods.

Main Methods:

• Utilized a multiscale convolutional neural network architecture.
• Implemented a novel dynamic triplet loss function that maximizes inter-class separation with an increasing margin.
• Evaluated the framework on three public bioacoustic datasets.

Main Results:

• The proposed deep metric learning framework significantly outperformed existing bioacoustic classification methods.
• Dynamic triplet loss demonstrated superior performance compared to cross-entropy loss in data-scarce conditions.
• The framework successfully achieved open-set classification, identifying unknown classes.

Conclusions:

• The proposed deep metric learning framework effectively addresses the challenge of limited labeled data in bioacoustics.
• Dynamic triplet loss is a powerful tool for improving classification accuracy in data-limited scenarios.
• The framework's open-set capability offers a significant advancement for real-world bioacoustic monitoring.