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Global Birdsong Embeddings Enable Superior Transfer Learning for Bioacoustic Classification by Ghani, Burooj; Denton, Tom; Kahl, Stefan; Klinck, Holger is a scholarly article available to read on EtoBox.

What is Global Birdsong Embeddings Enable Superior Transfer Learning for Bioacoustic Classification about?

Automated bioacoustic analysis aids understanding and protection of both marine and terrestrial animals and their habitats across extensive spatiotemporal scales, and typically involves analyzing vast collections of acoustic data. With the advent of deep learning models, classification of important signals from these datasets has markedly improved. These models power critical data analyses for research and decision-making in biodiversity monitoring, animal behaviour studies, and natural resource management. However, deep learning models are often data-hungry and require a significant amount of labeled training data to perform well. While sufficient training data is available for certain taxonomic groups (e.g., common bird species), many classes (such as rare and endangered species, many non-bird taxa, and call-type) lack enough data to train a robust model from scratch. This study investigates the utility of feature embeddings extracted from audio classification models to identify bioacoustic classes other than the ones these models were originally trained on. We evaluate models on diverse datasets, including different bird calls and dialect types, bat calls, marine mammals calls,

Author

Ghani, Burooj; Denton, Tom; Kahl, Stefan; Klinck, Holger

Published

2023

Language

EN