2015
DOI: 10.5545/sv-jme.2015.2781
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Automatic Recognition of Machinery Noise in the Working Environment

Abstract: A necessity for the suitable recognition of different machinery and equipment based on the sound they generate is constantly present and will increase in the future. The main motivation for the discrimination between different types of machinery sounds is to develop algorithms that can be used not only for final quality inspection but for the monitoring of the whole production line. The objective of our study is to recognize the operation of the individual machine in a production hall, where background noise l… Show more

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Cited by 7 publications
(3 citation statements)
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“…In recent years, we have done a lot of research on microphone arrays and environmental noise measurement. We have tried to mimic the human ability of acoustic spatial filtering as much as possible, [42,[78][79][80][81]126,127]. The use of spatial domain offers great potential for better identification of individual noise sources and to better understand their temporal and spatial dynamics.…”
Section: Automation Of Measurementsmentioning
confidence: 99%
See 1 more Smart Citation
“…In recent years, we have done a lot of research on microphone arrays and environmental noise measurement. We have tried to mimic the human ability of acoustic spatial filtering as much as possible, [42,[78][79][80][81]126,127]. The use of spatial domain offers great potential for better identification of individual noise sources and to better understand their temporal and spatial dynamics.…”
Section: Automation Of Measurementsmentioning
confidence: 99%
“…State-of-the-art approach to the identification, evaluation, and classification of environmental noise sources is presented in this paper. To mimic the human ability to spatially filter the acoustic environment (i.e., the Cocktail-Party effect), we have done much research and development in recent years in the area of microphone arrays for measuring environmental noise, [42,[78][79][80][81]126,127]. The implementation of the spatial domain to environmental noise measurements offers exceptional potential for more efficient identification of individual sources and clearer representation of the temporal and spatial dynamics of environmental noise.…”
Section: Automation Of Measurementsmentioning
confidence: 99%
“…Combined vibro-acoustic and electrical measurements have been used for the implementation of a system for automatic quality monitoring of vacuum-cleaner motors [12]. Modified MFCC, combined with the k-nearest neighbour classifier and multivariate Gaussian distribution, have been applied in order to automatically recognise machinery noise in a working environment [13]. Gaussian mixture model based classification methods have also been shown to be competitive and efficient methods in the field of fault detection and condition monitoring [14].…”
Section: Introductionmentioning
confidence: 99%