AUDIO SIGNAL PROCESSING AND NEURAL NETWORK FOR RAPID BIRD

Many domains need audio and visual modal data for exact study. Visual data might be hard to get, while aural data is easy. Using auditory data to generate visual data can assist here. This research presents a new audio-to-visual cross-modal creation method. A generative model creates graphics from aural data extracted by the suggested sound encoder. For realistic and well-classified pictures, this model must learn correct feature representation and correlations between produced images and auditory inputs. This study uses the AudioVisual Corresponding Bird (AVC-B) dataset, which comprises 10 bird species' sounds and photos. Experimental findings demonstrate that the suggested technique can create class-appropriate pictures and outperform state-of-the-art methods.