Audio Classifier

Audio classification, the task of automatically categorizing audio signals, aims to build robust and interpretable systems for diverse applications. Current research emphasizes improving model accuracy and efficiency using architectures like convolutional neural networks (CNNs) and transformers, often incorporating techniques such as dilated convolutions and multi-resolution ensembles. Significant effort focuses on enhancing interpretability through post-hoc explanation methods and designing inherently interpretable models, addressing concerns about the "black box" nature of deep learning. These advancements have implications for various fields, including healthcare diagnostics, environmental monitoring, and assistive technologies.