Audio Classification

Audio classification, the task of automatically categorizing sound recordings, aims to develop accurate and efficient systems for diverse applications, from music genre recognition to environmental sound monitoring and medical diagnostics. Current research emphasizes improving model robustness and generalization across various audio domains, focusing on architectures like transformers (e.g., AST, variants of ViT), state-space models (e.g., Mamba), and convolutional neural networks (CNNs), often incorporating techniques like contrastive learning and knowledge distillation to enhance performance. These advancements have significant implications for fields ranging from personalized music recommendations and assistive technologies for the hearing impaired to improved healthcare diagnostics and environmental monitoring.