  This paper presents an expert decision support system for time-invariant
aeroacoustic source classification. The system comprises two steps: first, the
calculation of acoustic properties based on spectral and spatial information;
and second, the clustering of the sources based on these properties. Example
data of two scaled airframe half-model wind tunnel measurements is evaluated
based on deconvolved beamforming maps. A variety of aeroacoustic features are
proposed that capture the characteristics and properties of the spectra. These
features represent aeroacoustic properties that can be interpreted by both the
machine and experts. The features are independent of absolute flow parameters
such as the observed Mach numbers. This enables the proposed method to analyze
data which is measured at different flow configurations. The aeroacoustic
sources are clustered based on these features to determine similar or atypical
behavior. For the given example data, the method results in source type
clusters that correspond to human expert classification of the source types.
Combined with a classification confidence and the mean feature values for each
cluster, these clusters help aeroacoustic experts in classifying the identified
sources and support them in analyzing their typical behavior and identifying
spurious sources in-situ during measurement campaigns.
