Squeak and rattle noise classification using radial basis function neural networks
-
-
[1] RMIT Melbourne
-
- Localización: Noise Control Engineering Journal, ISSN 0736-2501, Vol. 68, Nº. 4, 2020, págs. 283-293
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
-
Resumen
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). © 2020 Institute of Noise Control Engineering.
