Vehicle Noise Pattern Recognition by Self-Organizing Maps

Interior vehicle acoustics are in close connection with our quality opinion. The noise in vehicle interior is complex and can be considered as a sum of various sound emission sources. A nice sounding vehicle is objective of the development departments for car acoustics. In the process of manufacturing the targets for a qualitatively high-valuable sound must be maintained. However, it is possible that production errors lead to a deviation from the wanted vehicle interior sound. This will result in customer complaints where for example a rattling or squeak refers to a worn-out or defective component. Also in this case, of course, the vehicle interior noise does not fulfill the targets of the process of development. For both cases there is currently no possibility for automated analysis of the vehicle interior noise. In this paper an approach for automated analysis of vehicle interior noise by means of neural algorithms is investigated. The presented system analyses microphone signals from car interior measured at real environmental conditions. This is in contrast to well known techniques, as e.g. acoustic engine test bench. Self-Organizing Maps combined with feature selection algorithms are used for acoustic pattern recognition. The presented system can be used in production process as well as a standalone ECU in car.