Fusing multiple audio sensors for acoustic event detection

This paper presents an Internet-of-Things approach to fusing audio sensors towards the detection of audio events, in a meeting room scenario. The different types of audio sensors (microphones) and respective individual audio analysis modules are incorporated within the context of an IoT framework that follows a message-oriented architecture. Each individual audio analysis module is composed by a feature extraction stage and a Support-Vector-Machine (SVM) classifier. A fusion module is also adopted to combine the individual sensor-level decisions, in order to extract the final classification decision. A detailed experimental evaluation on a publicly available real-world dataset proves a rather significant performance boosting in terms of overall classification accuracy. In addition, the proposed architecture enables an easy-to-use training procedure that can easily handle any number of audio sensors and respective classifiers without any prior knowledge of the room's geometry or any other constraints regarding the topological condition of the sensors.