The present research deals with audio events detection in noisy environments for a multimedia surveillance application. In surveillance or homeland security most of the systems aiming to automatically detect abnormal situations are only based on visual clues while, in some situations, it may be easier to detect a given event using the audio information. This is in particular the case for the class of sounds considered in this paper, sounds produced by gun shots. The automatic shot detection system presented is based on a novelty detection approach which offers a solution to detect abnormality (abnormal audio events) in continuous audio recordings of public places. We specifically focus on the robustness of the detection against variable and adverse conditions and the reduction of the false rejection rate which is particularly important in surveillance applications. In particular, we take advantage of potential similarity between the acoustic signatures of the different types of weapons by building a hierarchical classification system.

This paper addresses the issue of automatic emotion recognition in speech. We focus on a type of emotional manifestation which has been rarely studied in speech processing: fear-type emotions occurring during abnormal situations (here, unplanned events where human life is threatened). This study is dedicated to a new application in emotion recognition -public safety. The starting point of this work is the definition and the collection of data illustrating extreme emotional manifestations in threatening situations. For this purpose we develop the SAFE corpus (situation analysis in a fictional and emotional corpus) based on fiction movies. It consists of 7 h of recordings organized into 400 audiovisual sequences. The corpus contains recordings of both normal and abnormal situations and provides a large scope of contexts and therefore a large scope of emotional manifestations. In this way, not only it addresses the issue of the lack of corpora illustrating strong emotions, but also it forms an interesting support to study a high variety of emotional manifestations. We define a task-dependent annotation strategy which has the particularity to describe simultaneously the emotion and the situation evolution in context. The emotion recognition system is based on these data and must handle a large scope of unknown speakers and situations in noisy sound environments. It consists of a fear vs. neutral classification. The novelty of our approach relies on dissociated acoustic models of the voiced and unvoiced contents of speech. The two are then merged at the decision step of the classification system. The results are quite promising given the complexity and the diversity of the data: the error rate is about 30%.

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