  Beamforming is an imaging tool for the investigation of aeroacoustic
phenomena and results in high dimensional data that is broken down to spectra
by integrating spatial Regions Of Interest. This paper presents two methods
that enable the automated identification of aeroacoustic sources in sparse
beamforming maps and the extraction of their corresponding spectra to overcome
the manual definition of Regions Of Interest. The methods are evaluated on two
scaled airframe half-model wind-tunnel measurements and on a generic monopole
source. The first relies on the spatial normal distribution of aeroacoustic
broadband sources in sparse beamforming maps. The second uses hierarchical
clustering methods. Both methods are robust to statistical noise and predict
the existence, location, and spatial probability estimation for sources based
on which Regions Of Interest are automatically determined.
