LIPAR, Primož ;ČUDINA, Mirko ;ŠTEBLAJ, Peter ;PREZELJ, Jurij . Automatic Recognition of Machinery Noise in the Working Environment. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 61, n.12, p. 698-708, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/automatic-recognition-of-machinery-noise-in-the-working-environment/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2015.2781.
Lipar, P., Čudina, M., Šteblaj, P., & Prezelj, J. (2015). Automatic Recognition of Machinery Noise in the Working Environment. Strojniški vestnik - Journal of Mechanical Engineering, 61(12), 698-708. doi:http://dx.doi.org/10.5545/sv-jme.2015.2781
@article{sv-jmesv-jme.2015.2781,
author = {Primož Lipar and Mirko Čudina and Peter Šteblaj and Jurij Prezelj},
title = {Automatic Recognition of Machinery Noise in the Working Environment},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {61},
number = {12},
year = {2015},
keywords = {machinery noise; machinery classification; k-NN classifier; multivariate Gaussian distribution classifier; frequency cepstral coefficients},
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 level is high and constantly changing. An experimental plan was designed and performed in order to confirm the hypothesis proposing that automatic speech recognition algorithms can be applied to automatic machine recognition. The design of the automatic machine recognition procedure used in our study was divided into three stages: feature extraction, training, and recognition (classification). Additionally, a traditional mel-frequency cepstral coefficient (MFCC) procedure was adjusted for machinery noise by using different filter compositions. Finally, two classifiers were compared, the k-NN classifier and the multivariate Gaussian distribution. The results of the experiment show that machinery noise features frequency cepstral coefficients (FCC) should be extracted by using linear filter compositions and processed with recognition algorithm based on the multivariate Gaussian distribution.},
issn = {0039-2480}, pages = {698-708}, doi = {10.5545/sv-jme.2015.2781},
url = {https://www.sv-jme.eu/sl/article/automatic-recognition-of-machinery-noise-in-the-working-environment/}
}
Lipar, P.,Čudina, M.,Šteblaj, P.,Prezelj, J. 2015 June 61. Automatic Recognition of Machinery Noise in the Working Environment. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 61:12
%A Lipar, Primož %A Čudina, Mirko %A Šteblaj, Peter %A Prezelj, Jurij %D 2015 %T Automatic Recognition of Machinery Noise in the Working Environment %B 2015 %9 machinery noise; machinery classification; k-NN classifier; multivariate Gaussian distribution classifier; frequency cepstral coefficients %! Automatic Recognition of Machinery Noise in the Working Environment %K machinery noise; machinery classification; k-NN classifier; multivariate Gaussian distribution classifier; frequency cepstral coefficients %X 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 level is high and constantly changing. An experimental plan was designed and performed in order to confirm the hypothesis proposing that automatic speech recognition algorithms can be applied to automatic machine recognition. The design of the automatic machine recognition procedure used in our study was divided into three stages: feature extraction, training, and recognition (classification). Additionally, a traditional mel-frequency cepstral coefficient (MFCC) procedure was adjusted for machinery noise by using different filter compositions. Finally, two classifiers were compared, the k-NN classifier and the multivariate Gaussian distribution. The results of the experiment show that machinery noise features frequency cepstral coefficients (FCC) should be extracted by using linear filter compositions and processed with recognition algorithm based on the multivariate Gaussian distribution. %U https://www.sv-jme.eu/sl/article/automatic-recognition-of-machinery-noise-in-the-working-environment/ %0 Journal Article %R 10.5545/sv-jme.2015.2781 %& 698 %P 11 %J Strojniški vestnik - Journal of Mechanical Engineering %V 61 %N 12 %@ 0039-2480 %8 2018-06-27 %7 2018-06-27
Lipar, Primož, Mirko Čudina, Peter Šteblaj, & Jurij Prezelj. "Automatic Recognition of Machinery Noise in the Working Environment." Strojniški vestnik - Journal of Mechanical Engineering [Online], 61.12 (2015): 698-708. Web. 20 Dec. 2024
TY - JOUR AU - Lipar, Primož AU - Čudina, Mirko AU - Šteblaj, Peter AU - Prezelj, Jurij PY - 2015 TI - Automatic Recognition of Machinery Noise in the Working Environment JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2015.2781 KW - machinery noise; machinery classification; k-NN classifier; multivariate Gaussian distribution classifier; frequency cepstral coefficients N2 - 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 level is high and constantly changing. An experimental plan was designed and performed in order to confirm the hypothesis proposing that automatic speech recognition algorithms can be applied to automatic machine recognition. The design of the automatic machine recognition procedure used in our study was divided into three stages: feature extraction, training, and recognition (classification). Additionally, a traditional mel-frequency cepstral coefficient (MFCC) procedure was adjusted for machinery noise by using different filter compositions. Finally, two classifiers were compared, the k-NN classifier and the multivariate Gaussian distribution. The results of the experiment show that machinery noise features frequency cepstral coefficients (FCC) should be extracted by using linear filter compositions and processed with recognition algorithm based on the multivariate Gaussian distribution. UR - https://www.sv-jme.eu/sl/article/automatic-recognition-of-machinery-noise-in-the-working-environment/
@article{{sv-jme}{sv-jme.2015.2781},
author = {Lipar, P., Čudina, M., Šteblaj, P., Prezelj, J.},
title = {Automatic Recognition of Machinery Noise in the Working Environment},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {61},
number = {12},
year = {2015},
doi = {10.5545/sv-jme.2015.2781},
url = {https://www.sv-jme.eu/sl/article/automatic-recognition-of-machinery-noise-in-the-working-environment/}
}
TY - JOUR AU - Lipar, Primož AU - Čudina, Mirko AU - Šteblaj, Peter AU - Prezelj, Jurij PY - 2018/06/27 TI - Automatic Recognition of Machinery Noise in the Working Environment JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 61, No 12 (2015): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2015.2781 KW - machinery noise, machinery classification, k-NN classifier, multivariate Gaussian distribution classifier, frequency cepstral coefficients N2 - 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 level is high and constantly changing. An experimental plan was designed and performed in order to confirm the hypothesis proposing that automatic speech recognition algorithms can be applied to automatic machine recognition. The design of the automatic machine recognition procedure used in our study was divided into three stages: feature extraction, training, and recognition (classification). Additionally, a traditional mel-frequency cepstral coefficient (MFCC) procedure was adjusted for machinery noise by using different filter compositions. Finally, two classifiers were compared, the k-NN classifier and the multivariate Gaussian distribution. The results of the experiment show that machinery noise features frequency cepstral coefficients (FCC) should be extracted by using linear filter compositions and processed with recognition algorithm based on the multivariate Gaussian distribution. UR - https://www.sv-jme.eu/sl/article/automatic-recognition-of-machinery-noise-in-the-working-environment/
Lipar, Primož, Čudina, Mirko, Šteblaj, Peter, AND Prezelj, Jurij. "Automatic Recognition of Machinery Noise in the Working Environment" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 61 Number 12 (27 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 61(2015)12, 698-708
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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 level is high and constantly changing. An experimental plan was designed and performed in order to confirm the hypothesis proposing that automatic speech recognition algorithms can be applied to automatic machine recognition. The design of the automatic machine recognition procedure used in our study was divided into three stages: feature extraction, training, and recognition (classification). Additionally, a traditional mel-frequency cepstral coefficient (MFCC) procedure was adjusted for machinery noise by using different filter compositions. Finally, two classifiers were compared, the k-NN classifier and the multivariate Gaussian distribution. The results of the experiment show that machinery noise features frequency cepstral coefficients (FCC) should be extracted by using linear filter compositions and processed with recognition algorithm based on the multivariate Gaussian distribution.