Mathematical models for the improvement of detection techniques of industrial noise sources from acoustic images

Mathematical models for the improvement of detection techniques of industrial noise sources from acoustic images

In this paper, a procedure for the detection of the sources of industrial noise and the evaluation of their distances is introduced. The above method is based on the analysis of acoustic and optical data recorded by an acoustic camera. In order to improve the resolution of the data, interpolation an...

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Bibliographic Details
Published in:Mathematical methods in the applied sciences Vol. 44; no. 13; pp. 10448 - 10459
Main Authors: Asdrubali, Francesco, Baldinelli, Giorgio, Bianchi, Francesco, Costarelli, Danilo, D'Alessandro, Francesco, Scrucca, Flavio, Seracini, Marco, Vinti, Gianluca
Format: Journal Article
Language:English
Published: Freiburg Wiley Subscription Services, Inc 15-09-2021
Subjects:
acoustic images
Acoustic noise
Acoustics
Algorithms
applied mathematics
beamforming
Data processing
Digital data
image reconstruction
Industrial noise
Interpolation
sampling Kantorovich algorithm
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Summary:In this paper, a procedure for the detection of the sources of industrial noise and the evaluation of their distances is introduced. The above method is based on the analysis of acoustic and optical data recorded by an acoustic camera. In order to improve the resolution of the data, interpolation and quasi interpolation algorithms for digital data processing have been used, such as the bilinear, bicubic, and sampling Kantorovich (SK). The experimental tests show that the SK algorithm allows to perform the above task more accurately than the other considered methods.
ISSN:0170-4214
1099-1476
DOI:10.1002/mma.7420