Engine sound comfortability: relevant sound quality parameters and classification
| Authors: | Coen Tom, Katholieke Universiteit Leuven, Belgium
Jans Noël, Katholieke Universiteit Leuven, Belgium
Van de Ponseele Patrick, LMS International, Belgium
Goethals Ivan, Katholieke Universiteit Leuven, Belgium
De Baerdemaeker Josse, Katholieke Universiteit Leuven, Belgium
De Moor Bart, Katholieke Universiteit Leuven, Belgium |
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| Topic: | 7.1 Automotive Control |
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| Session: | New Topics in Automotive Control |
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| Keywords: | Neural Networks, Models, Automobile Industry, Classification, Inputs |
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Abstract
In order to be able to shorten the design cycle, automobile manufacturers are interested in modelling the human perception of engine sounds.In the first part of the paper the relevant Sound Quality parameters for the prediction of engine sound comfortability are determined. The inputs are ordered with Automatic Relevance Determination and the obtained ranking is verified on the data. In the second part, models are presented to classify and compare cars on comfortability. Least Squares Support Vector Machines (LS-SVMs) is used for the classification. The influence of selecting the relevant inputs on the model performance is illustrated.
