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Mathematical models for the improvement of detection techniques of industrial noise sources from acoustic images
Author(s) -
Asdrubali Francesco,
Baldinelli Giorgio,
Bianchi Francesco,
Costarelli Danilo,
D'Alessandro Francesco,
Scrucca Flavio,
Seracini Marco,
Vinti Gianluca
Publication year - 2021
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.7420
Subject(s) - bilinear interpolation , bicubic interpolation , interpolation (computer graphics) , noise (video) , mathematics , sampling (signal processing) , algorithm , stairstep interpolation , computer science , artificial intelligence , computer vision , multivariate interpolation , image (mathematics) , statistics , filter (signal processing)
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.