  This paper presents an Expert Decision Support System for the identification
of time-invariant, aeroacoustic source types. The system comprises two steps:
first, acoustic properties are calculated based on spectral and spatial
information. Second, clustering is performed based on these properties. The
clustering aims at helping and guiding an expert for quick identification of
different source types, providing an understanding of how sources differ. This
supports the expert in determining similar or atypical behavior. A variety of
features are proposed for capturing the characteristics of the sources. These
features represent aeroacoustic properties that can be interpreted by both the
machine and by experts. The features are independent of the absolute Mach
number which enables the proposed method to cluster data measured at different
flow configurations. The method is evaluated on deconvolved beamforming data
from two scaled airframe half-model measurements. For this exemplary data, the
proposed support system method results in clusters that mostly correspond to
the source types identified by the authors. The clustering also provides the
mean feature values and the cluster hierarchy for each cluster and for each
cluster member a clustering confidence. This additional information makes the
results transparent and allows the expert to understand the clustering choices.
