Towards high-fidelity and controllable bioacoustic generation via enhanced diffusion learning

Generative modeling offers new opportunities for bioacoustics, enabling the synthesis of realistic animal vocalizations that could support biomonitoring efforts and supplement scarce data for endangered categories. However, directly generating bird call waveforms from noisy field recordings remains a major challenge. We propose BirdDiff , a generative framework designed to synthesize bird calls using a noisy dataset of 12 wild bird categories. The model incorporates a “zeroth layer” stage for multi-scale adaptive bird-call enhancement, followed by a diffusion-based generator conditioned on three modalities: Mel-frequency cepstral coefficients, category labels, and textual descriptions. The enhancement stage improves signal-to-noise ratio (SNR) while minimizing spectral distortion, achieving the highest SNR gain (＋10.45 dB) and lowest Itakura–Saito Distance (0.54) compared to three widely used non-training enhancement methods. We evaluate BirdDiff against a baseline generative model, DiffWave. Our method yields substantial improvements in generative quality metrics: Fréchet Audio Distance (FAD) (0.590 → 0.213), Jensen–Shannon Divergence (0.259 → 0.226), and Number of Statistically-Different Bins (7.33 → 5.58). To assess category-specific detail preservation, we use a ResNet50 classifier trained on the original dataset to identify generated samples, and further validate the generated calls using an external large-scale bird sound recognizer. Classification accuracy improves from 35.9% (DiffWave) to 70.1% (BirdDiff), with 8 of 12 categories exceeding 70% accuracy. These results demonstrate that BirdDiff enables high-fidelity, controllable bird call generation directly from noisy field recordings. Here, ‘high-fidelity’ refers to acoustic realism quantified by FAD and classifier agreement.