Acoustic event classification using a distributed microphone network with a GMM/SVM combined algorithm

This work proposes a system for acoustic event classification using signals acquired by a Distributed Microphone Network (DMN). The system is based on the combination of Gaussian Mixture Models (GMM) and Support Vector Machines (SVM). The acoustic event list includes both speech and non-speech events typical of seminars and meetings. The robustness of the system was investigated by considering two scenarios characterized by different types of trained models and testing conditions. Experimental results were obtained by using real-world data collected at two sites. The results in terms of classification error rate show that in each scenario the proposed system outperforms any single classifier based system.