Application of acoustic directional data for audio event recognition via HMM/CRF in perimeter surveillance systems

作者：Yusuf Syed A^*; Brown David J; Mackinnon Alan

来源：Robotics and Autonomous Systems, 2015, 72: 15-28.

DOI：10.1016/j.robot.2015.04.004

• Robotics
• Automation
• Hidden Markov models
• Conditional Random Fields
• Audio event detection
• Machine learning
• Artificial intelligence

Audio event detection (AED) and recognition is a signal processing and analysis domain used in a wide range of applications including surveillance, home automation and behavioral assessment. The field presents numerous challenges to the current state-of-the-art due to its highly nonlinear nature. High false alarm rates (FARs) in such applications particularly limit the capabilities of vision-based perimeter monitoring systems by inducing high operator dependence. On the other hand, conventional fence-based vibration detectors and pressure-driven "taut wires" offer high sensitivity at the cost of a high FAR due to debris, animals and weather.
This work reports an audio event identification methodology implemented as a test-bed system for a surveillance application to reduce FAR, maximize blind-spot coverage and improve audio event classification accuracy. The first phase utilizes a nonlinear autoregressive classifier to locate and classify discrete audio events via an exogenous sound direction variable to improve classifier confidence. The second phase implements a time-series-based system to recognize various audio activity groups from nominal everyday sound events such as traffic and muffled speech. The discretely labeled data is thus trained with HMM and Conditional Random Field classifiers and reports a substantial improvement in classification accuracies of indoor human activities.

• 出版日期2015-10