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.
Asdrubali, F., Baldinelli, G., Bianchi, F., Costarelli, D., D'Alessandro, F., Scrucca, F., et al. (2021). Mathematical models for the improvement of detection techniques of industrial noise sources from acoustic images. MATHEMATICAL METHODS IN THE APPLIED SCIENCES, 44(13), 10448-10459 [https://doi.org/10.1002/mma.7420].
Mathematical models for the improvement of detection techniques of industrial noise sources from acoustic images
Asdrubali, Francesco;
2021-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
