# Global birdsong embeddings enable superior transfer learning for bioacoustic classification

arXiv: https://arxiv.org/abs/2307.06292 · HTML: https://mlsanity.com/paper/2307.06292/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2023.

## Built on by (most cited first, 30 of 51)

- [Can Masked Autoencoders Also Listen to Birds?](https://mlsanity.com/paper/2504.12880/) (2025)
- [Perch 2.0: The Bittern Lesson for Bioacoustics](https://mlsanity.com/paper/2508.04665/) (2025)
- [The iNaturalist Sounds Dataset](https://mlsanity.com/paper/2506.00343/) (2025)
- [Self-Supervised Learning for Few-Shot Bird Sound Classification](https://mlsanity.com/paper/2312.15824/) (2023)
- [Foundation Models for Bioacoustics -- a Comparative Review](https://mlsanity.com/paper/2508.01277/) (2025)
- [AudioProtoPNet: An interpretable deep learning model for bird sound classification](https://mlsanity.com/paper/2404.10420/) (2024)
- [Towards Deep Active Learning in Avian Bioacoustics](https://mlsanity.com/paper/2406.18621/) (2024)
- [Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processing](https://mlsanity.com/paper/2501.05987/) (2025)
- [Massive Sound Embedding Benchmark (MSEB)](https://mlsanity.com/paper/2602.07143/) (2026)
- [Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics](https://mlsanity.com/paper/2404.16436/) (2024)
- [Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors](https://mlsanity.com/paper/2504.06710/) (2025)
- [Perch 2.0 transfers 'whale' to underwater tasks](https://mlsanity.com/paper/2512.03219/) (2025)
- [Generalization in birdsong classification: impact of transfer learning methods and dataset characteristics](https://mlsanity.com/paper/2409.15383/) (2024)
- [AVEX: What Matters for Animal Vocalization Encoding](https://mlsanity.com/paper/2508.11845/) (2025)
- [Robust detection of overlapping bioacoustic sound events](https://mlsanity.com/paper/2503.02389/) (2025)
- [All Thresholds Barred: Direct Estimation of Call Density in Bioacoustic Data](https://mlsanity.com/paper/2402.15360/) (2024)
- [WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound Database](https://mlsanity.com/paper/2402.17775/) (2024)
- [Finding Needles in the Haystack: Transductive Active Labeling in Ecology](https://mlsanity.com/paper/2606.03821/) (2026)
- [Synthetic data enables context-aware bioacoustic sound event detection](https://mlsanity.com/paper/2503.00296/) (2025)
- [An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed Pigeon](https://mlsanity.com/paper/2504.16276/) (2025)
- [Model Merging Improves Zero-Shot Generalization in Bioacoustic Foundation Models](https://mlsanity.com/paper/2511.05171/) (2025)
- [Linear probing enables Ship-Radiated Noise recognition with pretrained audio embeddings](https://mlsanity.com/paper/2601.08358/) (2026)
- [bacpipe: a Python package to make bioacoustic deep learning models accessible](https://mlsanity.com/paper/2604.11560/) (2026)
- [Dolph2Vec: Self-Supervised Representations of Dolphin Vocalizations](https://mlsanity.com/paper/2606.12503/) (2026)
- [MetaPerch: Learning from metadata for bioacoustics foundation models](https://mlsanity.com/paper/2607.14072/) (2026)
- [Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled Data](https://mlsanity.com/paper/2406.13579/) (2024)
- [Generative AI-based data augmentation for improved bioacoustic classification in noisy environments](https://mlsanity.com/paper/2412.01530/) (2024)
- [A Bird Song Detector for improving bird identification through Deep Learning: a case study from Do~nana](https://mlsanity.com/paper/2503.15576/) (2025)
- [No Free Lunch from Audio Pretraining in Bioacoustics: A Benchmark Study of Embeddings](https://mlsanity.com/paper/2508.10230/) (2025)
- [Dynamic Multi-Species Bird Soundscape Generation with Acoustic Patterning and 3D Spatialization](https://mlsanity.com/paper/2511.19275/) (2025)

## Abstract (arXiv)

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, and amphibians calls. The embeddings extracted from the models trained on bird vocalization data consistently allowed higher quality classification than the embeddings trained on general audio datasets. The results of this study indicate that high-quality feature embeddings from large-scale acoustic bird classifiers can be harnessed for few-shot transfer learning, enabling the learning of new classes from a limited quantity of training data. Our findings reveal the potential for efficient analyses of novel bioacoustic tasks, even in scenarios where available training data is limited to a few samples.