用KNN分类器对枪声进行分类

Proceedings of the Euro American Conference on Telematics and Information Systems Pub Date : 2018-11-12 DOI:10.1145/3293614.3293656

Francisco D. Pichardo-Morales, M. A. Acevedo-Mosqueda, S. L. Coronel

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引用次数: 0

摘要

本文提出了一种利用KNN分类器在高斯加性噪声存在和不存在的情况下对枪声进行检测和分类的系统。结果保证分类器在没有噪声且仅使用10个属性的情况下达到高达94%的性能。本文提出的属性易于获取，且在时域内。最后，对WEKA中流行的分类器进行了简要的比较，以确认KNN分类器在存在噪声时的优势。

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Classification of Gunshots with KNN Classifier

In this article a system of detection and classification of gunshots is proposed, which consists of using the KNN classifier in the presence and absence of Gaussian additive noise. The results guarantee that the classifier reaches up to 94 % of performance in the absence of noise and only using 10 attributes. The attributes proposed in this article are easy to obtain and in the time domain. Finally, brief comparison of popular classifiers in WEKA is also performed to confirm the KNN classifier's advantage in the presence of noise.

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Proceedings of the Euro American Conference on Telematics and Information Systems

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