Increasing The Precision Of Noise Source Detection System using KNN Method

Parlin Nando; Aji Gautama Putrada; Maman Abdurohman

doi:10.22219/kinetik.v4i2.757

Issue

Vol 4, No 2, May 2019

Issue Published : Mar 4, 2019

Increasing The Precision Of Noise Source Detection System using KNN Method

https://doi.org/10.22219/kinetik.v4i2.757

Parlin Nando

Universitas Telkom

Aji Gautama Putrada

Universitas Telkom

Maman Abdurohman

Universitas Telkom

Corresponding Author(s) : Parlin Nando

parlinnando@alumni.telkomuniversity.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol 4, No 2, May 2019
Article Published : May 28, 2019

Abstract

This paper proposes Accurate Noise Source Detection System using K-Nearest Neighbor (KNN) Method. Noise or sound intensity is usually measured in decibels (dB). In an educational environment the recommended noise index limit is 55 dB. It means that noise louder than that limit is prohibited. While a loud noise in a campus area occurred, it will be troublesome for the authorities to deal with the matter. This paper proposes a noise source detection system that can locate the position of the noise source. This system used Df analog V2 voice sensor for detecting the loud noise intensity. A microcontroller with WiFi capabilities will allow the system to communicate with an Internet of Things (IoT) platform that can perform a learning method to detect the location of the loud noise source. KNN method is used as the learning method. The result shows a user is able to get a warning related to the noise that occurs in an area at once. The precision position of the noise source can be detected with 70% average accuracy rate

Keywords

Loud noise noise source detection Internet of Things K-Nearest Neighbor Wireless Sensor Network

Nando, P., Putrada, A. G., & Abdurohman, M. (2019). Increasing The Precision Of Noise Source Detection System using KNN Method. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 4(2), 157-168. https://doi.org/10.22219/kinetik.v4i2.757

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References

P. H. T. Zannin, M. S. Engel, P. E. K. Fiedler, and F. Bunn, “Characterization of environmental noise based on noise measurements, noise mapping and interviews: A case study at a university campus in Brazil,” Cities, vol. 31, pp. 317–327, 2013.
D. Çolakkadıoğlu, M. Yücel, B. Kahveci, and Ö. Aydınol, “Determination of noise pollution on university campuses: a case study at Çukurova University campus in Turkey,” Environ. Monit. Assess., vol. 190, no. 4, p. 203, 2018.
J. S. Sudarsan and S. Nithiyanantham, “Study on Noise Pollution at SRM University Campus (South India–Asia),” Adv. Sci. Eng. Med., vol. 8, no. 11, pp. 896–905, 2016.
D. Thattai, J. S. Sudarsan, R. Sathyanathan, and V. Ramasamy, “Analysis of noise pollution level in a University campus in South India,” in IOP Conference Series: Earth and Environmental Science, 2017, vol. 80, p. 012053.
N. M. S. Purwaningsih et al., “Analysis of Noise Pollution: A Case Study of Malaysia’s University,” J. Int. Dent. Med. Res., vol. 11, no. 1, pp. 330–333, 2018.
B. N. Indratama, M. Abdurohman, and S. Prabowo, “Sistem Monitoring Kebisingan Lingkungan Pendidikan berbasis OpenMTC,” in Seminar Nasional Komputer dan Informatika, 2018.
J. Davila-Chacon, S. Magg, J. Liu, and S. Wermter, “Neural and statistical processing of spatial cues for sound source localisation,” in The 2013 International Joint Conference on Neural Networks (IJCNN), 2013, pp. 1–8.
J. A. Waller, S. L. Dance, and N. K. Nichols, “On diagnosing observation-error statistics with local ensemble data assimilation,” Q. J. R. Meteorol. Soc., vol. 143, no. 708, pp. 2677–2686, 2017.
Y. Zhang, S. Xing, Y. Zhu, F. Yan, and L. Shen, “RSS-based localization in WSNs using Gaussian mixture model via semidefinite relaxation,” IEEE Commun. Lett., vol. 21, no. 6, pp. 1329–1332, 2017.
L. Sun and Q. Cheng, “Indoor sound source localization and number estimation using infinite Gaussian mixture models,” in 2014 48th Asilomar Conference on Signals, Systems and Computers, 2014, pp. 1189–1193.
L. Pan and J. Li, “K-nearest neighbor based missing data estimation algorithm in wireless sensor networks,” Wirel. Sens. Netw., vol. 2, no. 02, p. 115, 2010.
N. A. Dieng, M. Charbit, C. Chaudet, L. Toutain, and T. B. Meriem, “Indoor localization in wireless networks based on a two-modes gaussian mixture model,” in 2013 IEEE 78th Vehicular Technology Conference (VTC Fall), 2013, pp. 1–5.
J. Hughes, J. Yan, and K. Soga, “Development of wireless sensor network using bluetooth low energy (BLE) for construction noise monitoring,” Int. J. Smart Sens. Intell. Syst., vol. 8, no. 2, pp. 1379–1405, 2015.
N. Prodi, A. Conti, F. Lodi, S. Bartoletti, and J. S. Dekhordi, “Localization-assisted indoor acoustical data monitoring,” 2015.
K. Chelliah, G. Raman, M. R. Aldeman, and H. J. Patel, “Demonstration of the possibilities of using a phased microphone array as the next-generation airport noise monitoring system,” Transp. Res. Rec., vol. 2600, no. 1, pp. 20–26, 2016.
P. Aumond et al., “Kriging-based spatial interpolation from measurements for sound level mapping in urban areas,” J. Acoust. Soc. Am., vol. 143, no. 5, pp. 2847–2857, 2018.
S. Pasha, “ThingSpeak based sensing and monitoring system for IoT with Matlab Analysis,” Int. J. New Technol. Res., vol. 2, no. 6, 2016.
P.-E. Danielsson, “Euclidean distance mapping,” Comput. Graph. Image Process., vol. 14, no. 3, pp. 227–248, 1980.
P. E. Touchette, R. F. MacDonald, and S. N. Langer, “A scatter plot for identifying stimulus control of problem behavior,” J. Appl. Behav. Anal., vol. 18, no. 4, pp. 343–351, 1985.
T. Saito and M. Rehmsmeier, “The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets,” PloS One, vol. 10, no. 3, pp. e0118432, 2015.

References

P. H. T. Zannin, M. S. Engel, P. E. K. Fiedler, and F. Bunn, “Characterization of environmental noise based on noise measurements, noise mapping and interviews: A case study at a university campus in Brazil,” Cities, vol. 31, pp. 317–327, 2013.

D. Çolakkadıoğlu, M. Yücel, B. Kahveci, and Ö. Aydınol, “Determination of noise pollution on university campuses: a case study at Çukurova University campus in Turkey,” Environ. Monit. Assess., vol. 190, no. 4, p. 203, 2018.

J. S. Sudarsan and S. Nithiyanantham, “Study on Noise Pollution at SRM University Campus (South India–Asia),” Adv. Sci. Eng. Med., vol. 8, no. 11, pp. 896–905, 2016.

D. Thattai, J. S. Sudarsan, R. Sathyanathan, and V. Ramasamy, “Analysis of noise pollution level in a University campus in South India,” in IOP Conference Series: Earth and Environmental Science, 2017, vol. 80, p. 012053.

N. M. S. Purwaningsih et al., “Analysis of Noise Pollution: A Case Study of Malaysia’s University,” J. Int. Dent. Med. Res., vol. 11, no. 1, pp. 330–333, 2018.

B. N. Indratama, M. Abdurohman, and S. Prabowo, “Sistem Monitoring Kebisingan Lingkungan Pendidikan berbasis OpenMTC,” in Seminar Nasional Komputer dan Informatika, 2018.

J. Davila-Chacon, S. Magg, J. Liu, and S. Wermter, “Neural and statistical processing of spatial cues for sound source localisation,” in The 2013 International Joint Conference on Neural Networks (IJCNN), 2013, pp. 1–8.

J. A. Waller, S. L. Dance, and N. K. Nichols, “On diagnosing observation-error statistics with local ensemble data assimilation,” Q. J. R. Meteorol. Soc., vol. 143, no. 708, pp. 2677–2686, 2017.

Y. Zhang, S. Xing, Y. Zhu, F. Yan, and L. Shen, “RSS-based localization in WSNs using Gaussian mixture model via semidefinite relaxation,” IEEE Commun. Lett., vol. 21, no. 6, pp. 1329–1332, 2017.

L. Sun and Q. Cheng, “Indoor sound source localization and number estimation using infinite Gaussian mixture models,” in 2014 48th Asilomar Conference on Signals, Systems and Computers, 2014, pp. 1189–1193.

L. Pan and J. Li, “K-nearest neighbor based missing data estimation algorithm in wireless sensor networks,” Wirel. Sens. Netw., vol. 2, no. 02, p. 115, 2010.

N. A. Dieng, M. Charbit, C. Chaudet, L. Toutain, and T. B. Meriem, “Indoor localization in wireless networks based on a two-modes gaussian mixture model,” in 2013 IEEE 78th Vehicular Technology Conference (VTC Fall), 2013, pp. 1–5.

J. Hughes, J. Yan, and K. Soga, “Development of wireless sensor network using bluetooth low energy (BLE) for construction noise monitoring,” Int. J. Smart Sens. Intell. Syst., vol. 8, no. 2, pp. 1379–1405, 2015.

N. Prodi, A. Conti, F. Lodi, S. Bartoletti, and J. S. Dekhordi, “Localization-assisted indoor acoustical data monitoring,” 2015.

K. Chelliah, G. Raman, M. R. Aldeman, and H. J. Patel, “Demonstration of the possibilities of using a phased microphone array as the next-generation airport noise monitoring system,” Transp. Res. Rec., vol. 2600, no. 1, pp. 20–26, 2016.

P. Aumond et al., “Kriging-based spatial interpolation from measurements for sound level mapping in urban areas,” J. Acoust. Soc. Am., vol. 143, no. 5, pp. 2847–2857, 2018.

S. Pasha, “ThingSpeak based sensing and monitoring system for IoT with Matlab Analysis,” Int. J. New Technol. Res., vol. 2, no. 6, 2016.

P.-E. Danielsson, “Euclidean distance mapping,” Comput. Graph. Image Process., vol. 14, no. 3, pp. 227–248, 1980.

P. E. Touchette, R. F. MacDonald, and S. N. Langer, “A scatter plot for identifying stimulus control of problem behavior,” J. Appl. Behav. Anal., vol. 18, no. 4, pp. 343–351, 1985.

T. Saito and M. Rehmsmeier, “The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets,” PloS One, vol. 10, no. 3, pp. e0118432, 2015.

Issue

Vol 4, No 2, May 2019

Increasing The Precision Of Noise Source Detection System using KNN Method

Corresponding Author(s) : Parlin Nando

Abstract

Keywords

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References

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