Squeak and rattle noise classification using radial basis function neural networks

被引:0
|
作者
Pogorilyi, Oleksandr [1 ]
Fard, Mohammad [1 ]
Davy, John [2 ]
机构
[1] RMIT, Sch Engn, Mech & Automot Engn, Melbourne, Vic, Australia
[2] RMIT, Sch Sci, Coll Sci Engn & Hlth, Melbourne, Vic, Australia
关键词
RECOGNITION; PERFORMANCE;
D O I
10.3397/1/376824
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary classification) and complex ones (multi class classification). (C) 2020 Institute of Noise Control Engineering.
引用
收藏
页码:283 / 293
页数:11
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