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

Abstract

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.

Study on the Application of Improved Audio Recognition Technology Based on Deep Learning in Vocal Music Teaching

“…Baidu applied the deep neural network to speech recognition research. On the basis of the VGGNet model, it integrated the multilayer convolutional neural network and the long short-term memory network structure to develop an end-to-end speech recognition technology [24]. Experiments show that the system reduces the recognition error rate by more than 10%.…”

Chinese Emergency Event Recognition Using Conv-RDBiGRU Model

“…In [9], it proposes an improved TF-PDF algorithm to recognize emergency event according to the relatively stable combination between hot words. e discretely labeled data is trained with HMM and Conditional Random Field classifiers and reports a substantial improvement in performance of event recognition [10]. On the basis of extracting traditional features [11], the features of semantic role SR are added and then CRFs are used to recognize event.…”