Sound Recognition

Sound recognition research aims to automatically identify and classify sounds from audio recordings, encompassing diverse applications from environmental monitoring to healthcare. Current efforts focus on improving the accuracy and efficiency of deep learning models, particularly convolutional neural networks (CNNs) and their variations like fully convolutional networks (FCNs) and pre-trained architectures (PANNs), often incorporating techniques like transfer learning and variational inference to enhance generalization and reduce computational demands for deployment on resource-constrained devices. These advancements are driving progress in various fields, including wildlife monitoring, assistive technologies, and smart home devices, by enabling more robust and efficient sound-based applications.