Creep Groan Noise Classification

Anže Železnik, Severin Huemer-Kals, Jure Murovec, Jurij Prezelj

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband


Creep groan is an unpleasant noise caused by stick-slip during braking at low speed and low to medium brake pressure. To automatically identify creep groan phenomena with a machine learning algorithm, the number of features extracted from accelerometers on the brakes needs to be as small as possible. This paper focuses on a brute force method for selecting the optimal combination of psychoacoustic features that can be used with the self-organising map to identify the creep groan noise of a vehicle's brakes. The number of classes was selected using principal component analysis and plotting the distances between two randomly selected data points. The results were evaluated using the maximum distance between SOM neurons and the R2 coefficient between the classified data and the subjective ratings of the brake noise. The results of this study show that the best psychoacoustic features for identifying creep groan are sharpness and fluctuation strength, and that unsupervised classification is more reliable than subjective classification of braking noise.
TitelThe 9th Congress of the Alps Adria Acoustics Association – Conference Proceedings
Redakteure/-innenMesterházy Beáta, Mikló Márkus
ErscheinungsortBudapest, Hungary
PublikationsstatusVeröffentlicht - 24 Sep. 2021
Veranstaltung9th Congress of the Alps Adria Acoustics Association: AAA 2021 - Budapest, Ungarn
Dauer: 23 Sep. 202124 Sep. 2021


Konferenz9th Congress of the Alps Adria Acoustics Association
KurztitelAAA 2021

Fields of Expertise

  • Mobility & Production


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