Sound Classification

Sound classification research aims to automatically categorize audio signals, with applications ranging from environmental monitoring to medical diagnostics. Current efforts focus on improving the accuracy and robustness of classification across diverse acoustic environments and datasets, employing deep learning models like Convolutional Neural Networks (CNNs), Transformers (including AST), and Recurrent Neural Networks (RNNs), often enhanced by techniques such as transfer learning, feature optimization, and denoising. These advancements are crucial for various fields, enabling more efficient biodiversity monitoring, improved medical diagnoses based on heart or lung sounds, and the development of assistive technologies for the hearing impaired.