Anuran call synthesis with diffusion models for enhanced bioacoustic classification under data scarcity - José Sebastián Ñungo Manrique; Francisco Gómez; Freddy Hernández-Romero (Ecological Informatics)
Addressing data scarcity in anuran bioacoustic monitoring, this study introduces diffusion probabilistic models (DPMs) for generating realistic calls and demonstrates their efficacy in data augmentation. A DPM framework combined with FID-based selection ensured synthetic call quality, with initial realism confirmed for Boana faber by human A/B tests. We then generated calls for nine species and evaluated DPM-based augmentation on severely imbalanced multi-species datasets. Our approach significantly improved F1 classification scores, especially for underrepresented species, outperforming a WGAN baseline. This highlights DPMs as a potent method to produce valuable synthetic bioacoustic data, enhancing AI-driven biodiversity monitoring.