Creep Groan Noise Classification

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.