BirdCODE: Scaling Zero-Shot Sound Event Detection for Bioacoustics

Summary

Image credit: Bejan Adrian / Pexels Key Takeaways BirdCODE is a supervised deep learning model that offers zero-shot sound event detection , enabling researchers to detect and classify bird vocalizations with temporal precision and without dataset-specific fine-tuning. The model was developed using a combination of training strategies using weakly labelled recordings, synthetic soundscapes, and pseudo-labelled data to perform sound event detection and classification across more than 9,000 bird species . Applying BirdCODE to more than one million recordings demonstrates how large-scale temporal annotation can enable new analyses of vocal behaviour across species, seasons, geography, and mixed-species interactions . BirdCODE, the model weights, and large-scale detections are openly available on GitHub to support future research in Animal Language Processing. This paper is a preprint and currently under review. Sound Event Detection: A Bottleneck in Bioacoustics Sound event detection remains one of the central challenges in bioacoustics. While recent machine learning models have dramatically improved species classification, detecting the precise temporal boundaries of individual vocalizations remains difficult, particularly at large taxonomic scales where strongly labelled training data are scarce. For researchers interested in animal communication, this limitation is significant. Studying communication at scale depends on accurate temporal annotation. Understanding interactions between individuals often requires knowing precisely when each vocalization begins and ends relative to every other vocalization. Solving this challenge is therefore a prerequisite for studying animal communication at scale. Introducing BirdCODE To address this challenge, we developed BirdCODE (Bird Communication Detector), a deep learning model for large-scale sound event detection and classification across more than 9,000 bird species. Unlike conventional sound event detection systems, BirdCODE does not rely on extensive strongly labelled training datasets. Instead, it learns from the vast collection of weakly labelled recordings contributed through citizen-science initiatives, combining them with synthetic soundscapes and pseudo-labelled predictions in a multi-stage training pipeline. Figure 1: An overview of BirdCODE model training. Strongly labeled data are converted to per-frame…