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Automatic source localization and spectra generation from sparse beamforming maps.

A Goudarzi¹, C Spehr¹, S Herbold²

¹Institute of Aerodynamics and Flow Technology, German Aerospace Center (DLR), Göttingen, Germany.

The Journal of the Acoustical Society of America

|October 2, 2021

Summary

This summary is machine-generated.

This study introduces automated methods for identifying aeroacoustic sources in beamforming data. These techniques overcome manual region definition, improving the analysis of sound sources in complex aerodynamic flows.

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Area of Science:

Aeroacoustics
Acoustic Imaging
Signal Processing

Background:

Beamforming is crucial for investigating aeroacoustic phenomena, generating high-dimensional data.
Current methods require manual definition of regions of interest (ROIs) for spectral analysis.
This limits efficiency and objectivity in identifying aeroacoustic sources.

Purpose of the Study:

To develop and evaluate automated methods for identifying aeroacoustic sources in sparse beamforming maps.
To enable automatic extraction of spectra corresponding to identified sources.
To overcome the limitations of manual ROI definition in aeroacoustic analysis.

Main Methods:

Two novel methods for automated aeroacoustic source identification were developed.
Method 1 utilizes the spatial normal distribution of broadband sources in beamforming maps.
Method 2 employs hierarchical clustering techniques for source localization.

Main Results:

Both automated methods successfully identified aeroacoustic sources in wind tunnel measurements.
The methods accurately predicted source existence, location, and spatial probability.
Automated ROI determination proved robust against statistical noise.

Conclusions:

Automated identification and spectral extraction of aeroacoustic sources are feasible.
The proposed methods offer an objective and efficient alternative to manual ROI definition.
These advancements enhance the analysis of aeroacoustic phenomena using beamforming data.