Towards High-Fidelity and Controllable Bioacoustic Generation via Enhanced Diffusion Learning

2025-08-29

AI Summary

Generating high-fidelity bird vocalization waveforms from noisy field recordings remains challenging. Method: We propose the first end-to-end, high-fidelity, and semantically controllable bird-call synthesis framework. It introduces a multi-scale adaptive “zeroth-layer” preprocessing module and integrates a tri-modal conditional diffusion model (DiffWave), jointly conditioned on Mel spectrograms, species labels, and textual descriptions. A training-free signal enhancement technique is embedded in the frontend for lightweight denoising. Results: The generated audio achieves a 10.45 dB SNR improvement and significantly reduced spectral distortion. Species classification accuracy rises from 35.9% to 70.1%, with 8 out of 12 species exceeding 70%. All perceptual and objective quality metrics surpass those of baseline methods. This work establishes the first paradigm for directly synthesizing high-quality, semantically controllable bird-call waveforms from noisy field recordings, enabling scalable bioacoustic monitoring and data augmentation for endangered species.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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 species. 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 from a noisy dataset of 12 wild bird species. 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, species 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 (0.590 to 0.213), Jensen-Shannon Divergence (0.259 to 0.226), and Number of Statistically-Different Bins (7.33 to 5.58). To assess species-specific detail preservation, we use a ResNet50 classifier trained on the original dataset to identify generated samples. Classification accuracy improves from 35.9% (DiffWave) to 70.1% (BirdDiff), with 8 of 12 species exceeding 70% accuracy. These results demonstrate that BirdDiff enables high-fidelity, controllable bird call generation directly from noisy field recordings.

Problem

• Generate high-fidelity bird calls from noisy field recordings
• Improve signal-to-noise ratio while minimizing spectral distortion
• Enable controllable generation through multi-modal conditioning techniques

Innovation

• Multi-scale adaptive bird-call enhancement layer
• Diffusion generator with three conditioning modalities
• Direct generation from noisy field recordings