In the present paper we report on a recent effort that resulted in the establishment of a unique multimodal database, referred to as the PROMETHEUS database. This database was created in support of research and development activities, performed within the European Commission FP7 PROMETHEUS project, aiming at the creation of a framework for monitoring and interpretation of human behaviours in unrestricted indoors and outdoors environments. In the present paper we discuss the design and the implementation of the audio part of the database and offer statistical information about the audio content. Specifically, it contains single-person and multi-person scenarios, but also covers scenarios with interactions between groups of people. The database design was conceived with extended support of research and development activities devoted to detection of typical and atypical events, emergency and crisis situations, which assist for achieving situational awareness and more reliable interpretation of the context in which humans behave. The PROMETHEUS database allows for embracing a wide range of real-world applications, including smart-home and human-robot interaction interfaces, indoors/outdoors public areas surveillance, airport terminals or city park supervision, etc. A major portion of the PROMETHEUS database will be made publically available by the end of year 2010.

This paper describes a corpus consisting of audio data for automatic space monitoring based solely on the perceived acoustic information. The particular database is created as part of a project aiming at the detection of abnormal events, which lead to life-threatening situations or property damage. The audio corpus is composed of vocal reactions and environmental sounds that are usually encountered in atypical situations. The audio data is composed of three parts: Phase I - professional sound effects collections, Phase II recordings obtained from action and drama movies and Phase III - vocal reactions related to real-world emergency events as retrieved from television, radio broadcast news, documentaries etc. The annotation methodology is given in details along with preliminary classification results and statistical analysis of the dataset regarding Phase I. The main objective of such a dataset is to provide training data for automatic recognition machines that detect hazardous situations and to provide security enhancement in public environments, which otherwise require human supervision.