Automatic source localization and spectra generation from sparse beamforming maps
This work provides an automated solution for aeroacoustic engineers to identify sound sources and extract their spectra, improving efficiency and robustness compared to manual methods.
This paper addresses the challenge of manually defining Regions Of Interest in aeroacoustic beamforming maps by introducing two automated methods for identifying aeroacoustic sources and extracting their spectra. These methods, one based on spatial normal distribution and the other on hierarchical clustering, were evaluated using wind-tunnel measurements of airframe half-models and a generic monopole source.
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.