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Sound learning-based event detection for acoustic surveillance sensors

机译：基于声音监视传感器的基于学习的事件检测

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摘要

This study proposes an event detection technique for acoustic surveillance that detects emergency situations by using acoustic sensors. Most surveillance systems have widely depended on visual data recorded by closed-circuit television (CCTV) cameras, but more intelligent systems are now beginning to use audio information for more reliable detection of emergency situations. Most of the conventional studies on acoustic event detection adopt limited types of acoustic data and are based on simple algorithms, such as energy-based determination. Thus, these approaches are easily realized, but may induce serious detection errors in real-world applications. In this study, we propose an event detection technique based on a sound-learning algorithm to be adopted by realtime acoustic surveillance systems. One main process of this technique is to construct acoustic models via learning algorithms from sound data collected according to types of acoustic events. The models are used to determine whether audio streams entering an acoustic sensor refer to the events or not. In event detection experiments performed in an outdoor environment, the proposed approach outperformed conventional approaches in the real-time detection of acoustic events.
机译：本研究提出了一种用于声学监视的事件检测技术，用于通过使用声学传感器检测紧急情况。大多数监视系统都广泛依赖于闭路电视（CCTV）摄像机记录的可视数据，但现在更多的智能系统现在开始使用音频信息以便更可靠地检测紧急情况。关于声学事件检测的大多数传统研究采用有限类型的声学数据，并基于简单的算法，例如基于能量的确定。因此，这些方法很容易实现，但可能会引起真实应用中的严重检测误差。在本研究中，我们提出了一种基于实时声学监视系统采用的音响学习算法的事件检测技术。该技术的一个主要过程是通过根据声学事件类型收集的声音数据来构建声学模型。该模型用于确定进入声学传感器的音频流是否参考事件。在在室外环境中进行的事件检测实验中，所提出的方法在原始事件的实时检测中表现出常规方法。

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