Automatic source localization and spectra generation from sparse beamforming maps

Abstract

Beamforming is an imaging tool for the investigation of aeroacoustic phenomena and results in high-dimensional data that are 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.

Global, and Local Optimization Beamforming for Broadband Sources

“…The first column shows the spatial position of the reconstructed source-parts. A source-part is defined as a CLEAN-SC result in a single location and frequency with P (x, f ) ≥ 0 Pa 2 Hz −1 , that -once in- tegrated through a ROI -gives a full source spectrum [3]. The source part color indicats their frequency, and their opacity indicates their log level log(P + 1), normalized for each frequency to (0, 1).…”

“…To evaluate the proposed method on real data, we use the presented open wind tunnel data from the SIND method [3] at Mach M = 0 as case 5a). The data features an equidistant 7x7 microphone array −0.27 m ≤ x 1,2 ≤ 0.27 m, x 3 = −0.65 m, with ∆x 1 = ∆x 2 = 0.09 m, and a generic monopole source (streamlined housing with a r = 2.5 mm opening at the downstream end) in a reverberationfree environment.…”

Expert Decision Support System for Aeroacoustic Classification from Deconvolved Beamforming Maps

“…The proposed expert decision support system is based on the feature calculation from source spectra. We obtain these spectra either from single source measurements or from measuring multiple sources at the same time with beamforming 11 using either manually defined ROIs or using automatic source identification methods such as SIND 5 . For the data presented in this paper, we use SIND to obtain the source positions and source spectra.…”

“…For this paper, we use deconvolved beamforming maps of the scaled air-frame models of a Dornier 728 (Do728) 2 and an Airbus A320 (A320) 3 as example data to derive these features, discuss their usefulness, and specify a proof-of-concept implementation. We employ the Source Identification based on spatial Normal Distributions (SIND) 5 approach to identify aeroacoustic sources and obtain their spectra from the beamforming maps. Since there is no ground-truth for the datasets, we present our manual evaluation of the airframe source types with exemplary source spectra and our decision choices to the reader.…”

“…The Cross-Spectral density Matrices (CSM) were calculated using Welch's method with a block size of 1024 samples with 50 % overlap. The beamforming was performed using conventional beamforming 11 and CLEAN-SC deconvolution 17 on a regular grid with a focus point resolution of ∆x 1 = ∆x 2 = 5 × 10 −3 m. The acoustic source positions and Region Of Interests (ROI) were automatically detected using the SIND method 5 .…”