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.