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 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.

Automated summary

This paper introduces a procedure for detecting the sources of industrial noise and evaluating their distances based on analysis of acoustic and optical data recorded by an acoustic camera. Interpolation and quasi interpolation algorithms such as bilinear, bicubic, and sampling Kantorovich (SK) are used to improve data resolution, with experimental tests demonstrating that the SK algorithm performs the task more accurately than other methods considered.