A preliminary study of acoustic events classification with factor analysis in meeting rooms

Abstract

The classification of acoustic events is useful to describe the scene and can contribute to improve the robustness of different speech technologies. However, the events are usually overlapped with speech or other sounds. This work proposes an approach based on Factor Analysis to compensate the variability of the acoustic events due to overlap with speech. The system is evaluated in the CLEAR evaluation database composed of recordings in meeting rooms where the acoustic events have been spontaneously generated in five different locations. The experiments are divided in two sets. Firstly, isolated acoustic events are used as development to analyze and evaluate parameters of the Factor Analysis system. Secondly, the system is compared to a baseline based on Gaussians Mixture Models with Hidden Markov Models. The Factor Analysis approach improves the total error rate due to the variability compensation of overlapped segments.