Abstract
Using neural networks, discriminant analysis and regression, a model
for annoyance assessment of vehicle interior noise situations is
empirically estimated. The data base for the estimate consists of
12 different vehicle-speed pairs for which 24 subjective assessments
are available. Descriptive statistical parameters of the dataset
are presented. The estimation of annoyance scores using frequency
spectra of the noise signals shows good results, for both the connectionist
and the classical statistical approaches. The influence of measuring
time and of the frequency range on the classification and on the
generalization is evaluated. The background of the used methods is
briefly described. Their advantages and disadvantages concerning
the application to the data and the interpretation of the estimated
classifier concerning generalization of the knowledge extracted by
these “black box” tools are discussed and compared.
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