Diffusion-based Audio-to-Visual Generation for High-Quality Bird Images

Abstract

Accurately identifying bird species from their vocalizations and generating corresponding bird images is still a challenging task dueto limited training data and environmental noise in audio data. To address this limitation, this paper introduces a diffusion-basedaudio-to-image generation approach that satisfies both the need to accurately identify bird sounds and generate bird images. The mainidea is to use a conditional diffusion model to handle the complexities of bird audio data, such as pitch variations and environmentalnoise while establishing a robust connection between the auditory and visual domains. This enables the model to generate high-qualitybird images based on the given bird audio input. Plus, the proposed approach is integrated with deep audio processing to enhance itscapabilities by meticulously aligning audio features with visual information and learning to map intricate acoustic patterns to correspondingvisual representations. Experimental results demonstrate the effectiveness of the proposed approach in generating better images for birdclasses compared to previous methods