Variability Modelling for Audio Events Detection in Movies.
Résumé
Detecting audio events in Hollywood movies is a complex task due to the presence of variability between the soundtracks of the movies. This inter-movies variability is shown to impair the audio events detection results in a realistic framework. In this article, we propose to model the variability using a factor analysis technique, which we then use to compensate the audio features. The factor analysis compensation is validated using the state-of-the-art system based on multiple audio words sequences and contextual Bayesian networks which we previously developed in Penet et al. (2013). Results obtained on the same publicly available dataset for the detection of gunshots and explosions show an improvement in the handling of the variability, while keeping the robustness capabilities of the previous system. Furthermore, the system is applied to the detection of screams and proves its ability to generalise to other types of events. The obtained results also emphasise the fact that, in addition to modelling variability, adding concepts in the system may also be beneficial for the precision rates