Classification of Gunshots with KNN Classifier
Authors: 
Francisco D. Pichardo-Morales, Marco A. Acevedo-Mosqueda, Sandra L. Gomez-CoronelAuthors Info & Claims
Article No.: 23, Pages 1 - 5
Abstract
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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- Classification of Gunshots with KNN Classifier
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Information
Published In
November 2018
297 pages
ISBN:9781450365727
DOI:10.1145/3293614
- Co-chairs:
- Gabriel Paillard,
- Francisco Milton Mendes Neto,
- Program Chairs:
- Emanuel Ferreira Coutinho,
- Rogério Patrício Chagas do Nascimento
Copyright © 2018 ACM.
© 2018 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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- EATIS: Euro American Association on Telematics and Information Systems
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 November 2018
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EATIS '18
EATIS '18: Euro American Conference on Telematics and Information Systems
November 12 - 15, 2018
Fortaleza, Brazil
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Overall Acceptance Rate 17 of 64 submissions, 27%
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