Real-time sensing and acoustic scene characterization for security applications

Real-time acoustic scene analysis has several applications such as homeland security, surveillance, and monitoring. The development of a collaborative networking infrastructure can be valuable in scene analysis since feature parameters can be extracted locally (at the node level) and combined at the base station. In this context, distributed and agile wireless sensor networks (WSNs) have been of particular interest recently. In this paper, we propose real-time voice scene characterization algorithms for use in a wireless sensor network. Voice scene analysis is accomplished using a speech discriminator, a gender classifier, a system for recognizing the state of emotion, and an estimator of the number of speakers in an area of interest. Real-time implementations of these algorithms are accomplished using Crossbow motes and TI DSP boards, configured to operate in a wireless sensor network. A series of experiments are presented that characterize the performance of the algorithms under different conditions.