@article {Pogorilyi:2020:0736-2501:283,
title = "Squeak and rattle noise classification using radial basis function neural networks",
journal = "Noise Control Engineering Journal",
parent_itemid = "infobike://ince/ncej",
publishercode ="ince",
year = "2020",
volume = "68",
number = "4",
publication date ="2020-07-01T00:00:00",
pages = "283-293",
itemtype = "ARTICLE",
issn = "0736-2501",
url = "https://ince.publisher.ingentaconnect.com/content/ince/ncej/2020/00000068/00000004/art00004",
doi = "doi:10.3397/1/376824",
keyword = "74.4, 21.2",
author = "Pogorilyi, Oleksandr and Fard, Mohammad and Davy, John and Mechanical and Automotive Engineering, School of Engineering,RMIT and Mechanical and Automotive Engineering, School of Engineering,RMIT and Mechanical and Automotive Engineering, School of Engineering,RMIT and Mechanical and Automotive Engineering, School of Engineering,RMIT and Mechanical and Automotive Engineering, School of Engineering,RMIT",
abstract = "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 clas- sification) and complex ones (multi class classification).",
}