@article{M08194F67,
title = "Diffusion-based Audio-to-Visual Generation for High-Quality Bird Images",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2025",
issn = "2287-5905",
doi = "https://doi.org/10.3745/TKIPS.2025.14.3.135",
author = "Adel Toleubekova, Joo Yong Shim, XinYu Piao, Jong-Kook Kim",
keywords = "Audio-to-visual generation, Diffusion models, Image generation, Audio features, Multi-modal generation",
abstract = "Accurately identifying bird species from their vocalizations and generating corresponding bird images is still a challenging task due 
to limited training data and environmental noise in audio data. To address this limitation, this paper introduces a diffusion-based 
audio-to-image generation approach that satisfies both the need to accurately identify bird sounds and generate bird images. The main 
idea is to use a conditional diffusion model to handle the complexities of bird audio data, such as pitch variations and environmental 
noise while establishing a robust connection between the auditory and visual domains. This enables the model to generate high-quality 
bird images based on the given bird audio input. Plus, the proposed approach is integrated with deep audio processing to enhance its 
capabilities by meticulously aligning audio features with visual information and learning to map intricate acoustic patterns to corresponding 
visual representations. Experimental results demonstrate the effectiveness of the proposed approach in generating better images for bird 
classes compared to previous methods"
}