基于卷积神经网络的枪响检测

Jakub Bajzik, J. Prinosil, D. Koniar
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引用次数: 10

摘要

本文主要对音频信号中的信号处理和事件识别方法进行了分析,并对所选方法在实际应用中的实现进行了阐述。可识别的事件是混合了背景声音(如交通噪音、人声、动物声和其他形式的环境声音)的枪声。该算法适用于爆炸探测,可作为安全系统的一部分,用于监控专门用于储存危险材料的仓库或场所。对于事件分类和类识别,使用免费的机器学习框架TensorFlow和Keras。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gunshot Detection Using Convolutional Neural Networks
The main paper deals with the analysis of the methods of signal processing and events recognition in the audio signal and the implementation of the selected method in real use. Recognized events are gunshots mixed with a background sound such as traffic noise, human voice, animal sounds and other forms of environmental sounds. The proposed algorithm adapted for explosion detection can be used as part of a security system for monitoring depots or places dedicated to storing dangerous materials. For events classification and class recognition, the freely available machine learning frameworks TensorFlow and Keras are used.
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