Application of acoustic directional data for audio event recognition via HMM/CRF in perimeter surveillance systems
Audio event modeling platform for improved threat identification in surveillance is reported. Discrete sound events can be identified accurately via sound energy and direction features. Time-series audio event classification achieves a higher accuracy with exogenous variables. Better audio event detection helps minimize video surveillance blindspots. Accurate audio activity classification reduce false positives and minimize operator burden.
audio event detection robotics automation hidden markov models conditional random fields machine learning artificial intelligence