A system for distributed audio classification using sparse representation over cloud for IOT

In a cloud setting where audio is intercepted from multiple nodes, it is of interest of identify the type of audio present at each node. Each node can be seen as an IOT (Internet of Things) device. Audio type can be speech by a particular speaker, different kinds of music or background noises, whose monitoring is useful for various IOT applications. A supervised audio classification system using sparse representation over a cloud network is presented. In this system, dictionaries are learnt from different audio streams on multiple nodes in the training stage. Audio classification is done separately on different nodes using distributed sparse representation, avoiding any centralized processing. Both training and testing is done in a distributed manner, and the final estimate of the audio type is arrived by consensus among the nodes. Given an audio segment at any node, distributed sparse coding is used to classify the segment into one of the audio classes using the different dictionary models learnt at different nodes.