Issue |
E3S Web Conf.
Volume 130, 2019
The 1st International Conference on Automotive, Manufacturing, and Mechanical Engineering (IC-AMME 2018)
|
|
---|---|---|
Article Number | 01035 | |
Number of page(s) | 7 | |
DOI | https://doi.org/10.1051/e3sconf/201913001035 | |
Published online | 15 November 2019 |
Intelligent Automatic V6 and V8 Engine Sound Detection Based on Artificial Neural Network
1
Automotive and Robotics Engineering Department, BINUS ASO School of Engineering, Bina
Nusantara University,
Alam Sutera Boulevard no. 1, Alam Sutera-Serpong,
Tangerang-Banten,
15325,
Indonesia
2
Computer Engineering Department, BINUS ASO School of Engineering, Bina Nusantara University,
Jl. K. H. Syahdan No. 9, Kemanggisan,
Palmerah, Jakarta,
11480,
Indonesia
3
Faculty of Engineering and Built Enviroment, UCSI University,
No. 1, Jalan Menara Gading, UCSI Heights (Taman Connaught), Cheras,
56000,
Kuala Lumpur,
Malaysia
* Corresponding author: wastuti@binus.edu
The sound of V6 or V8 engines has its own cultural appeal that cannot be replaced by the modern four-cylinder naturally aspirated or turbocharged engines. The identification of the type of engine by the sound is not an easy task, even for the professionals. An intelligent system that can identify V6 to V8 engines from various cars will give an insight of the features in the engine sounds that characterized the two different engines. In this work, an Artificial Neural Network (ANN) approach is applied for identifying cylinder of the engine based on the engine sound identification is proposed. The recorded sound of the engine is then processed in order to get some features which later be used in the proposed system. The Fast Fourir Transform (FFT) is adopted as a feature and later used as input to the Artificial Neural Network (ANN) based identifier. The Experimental results confirm the effectiveness of the proposed intelligent automatic six cylinder and eight cylinder engine based on Fast Fourier Transform (FFT) and Artificial Neural Network (ANN), since it resulting the training and testing accuracy of 100 % and 100 %, respectively.
Key words: Automatics petrol identification / fast fourier transform / support vector machine
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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