Robust Bioacoustic Detection via Richly Labelled Synthetic Soundscape Augmentation

TL;DR

This paper presents a synthetic soundscape generation framework that enhances bioacoustic detection robustness, reduces manual labelling effort, and improves model generalisation from limited data.

Contribution

It introduces a novel synthetic data augmentation method that creates richly labelled soundscapes, enabling robust bioacoustic detection with minimal source data.

Findings

Models trained on synthetic data generalise well to real-world soundscapes.

Performance remains high despite reduced diversity in source vocalisations.

Synthetic data generation significantly reduces manual labelling effort.

Abstract

Passive Acoustic Monitoring (PAM) analysis is often hindered by the intensive manual effort needed to create labelled training data. This study introduces a synthetic data framework to generate large volumes of richly labelled training data from very limited source material, improving the robustness of bioacoustic detection models. Our framework synthesises realistic soundscapes by combining clean background noise with isolated target vocalisations (little owl), automatically generating dynamic labels like bounding boxes during synthesis. A model fine-tuned on this data generalised well to real-world soundscapes, with performance remaining high even when the diversity of source vocalisations was drastically reduced, indicating the model learned generalised features without overfitting. This demonstrates that synthetic data generation is a highly effective strategy for training robust bioacoustic detectors from small source datasets. The approach significantly reduces manual labelling effort, overcoming a key bottleneck in computational bioacoustics and enhancing ecological assessment capabilities.