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Sound quality targets for new vehicles are currently specified by jury evaluation techniques based upon listening studies in a sound laboratory. However, jury testing is costly, time consuming and at present there are no methods to include customer expectations or brand requirements. This paper describes a neural computing approach that is being developed to generate knowledge and tools to enable objective measures of a product's sound to be converted into a prediction of the subjective impression of potential customers without carrying out the traditional jury evaluation tests.

All new European vehicles face strict drive-by noise regulations. It would help vehicle designers if they could predict drive-by noise given parameters available early in the design process. The large amount of data from previous tests suggests a new approach, using neural networks. This paper introduces neural networks and describes how to apply them to the prediction problem. The selection of suitable inputs and amount of data required is discussed. The problem can be simplified by first predicting vehicle performance. Interim results for a vehicle performance neural network are presented. Further work towards a drive-by noise neural network is proposed.