Monitoring System of Environment Noise and Pattern Recognition

cb5cd137-fa4e-4265-afde-7ca7765a17f420210324051435844naunmdt@crossref.orgMDT DepositInternational Journal of Energy and Environment2308-100710.46300/91012http://www.naun.org/cms.action?id=3043324202132420211510.46300/91012.2021.15https://www.naun.org/cms.action?id=23309Monitoring System of Environment Noise and Pattern RecognitionLuis Pastor SánchezFernándezInstituto Politécnico Nacional, Centro de Investigación en Computación Av. Juan de Dios Bátiz s/n, Col. Nueva Industrial Vallejo, C.P. 07738, México, D.F.Luis A. SánchezPérezInstituto Politécnico Nacional, Centro de Investigación en Computación Av. Juan de Dios Bátiz s/n, Col. Nueva Industrial Vallejo, C.P. 07738, México, D.F.José J. CarbajalHernándezInstituto Politécnico Nacional, Centro de Investigación en Computación Av. Juan de Dios Bátiz s/n, Col. Nueva Industrial Vallejo, C.P. 07738, México, D.F.This paper presents an overview of the wireless monitoring system of environment noise, placed throughout Historical Centre of México City which represents an attractive technological innovation. It takes permanent measurements of noise levels and stream the data back to the main monitoring station every five minutes and the measurements of noise produced during the take-off in a location of the International Airport. The data acquisition is made at 25 KHz at 24 bits resolution. This work allows analyzing the urban noise level and its frequency range. Additionally, a computational model for aircraft recognition using take-off noise spectral features is analyzed based on other previous results. Eight aircraft categories with all signals acquired in real environments are used. The model has an identification level between 65 and 70% of success. These spectral features are used to allow comparison with other aircraft recognition methods using speech processing techniques in real environments. This system type helps to foresee potential effects to health of environment noise.324202132420211017https://www.naun.org/main/NAUN/energyenvironment/2021/a042011-002(2021).pdf10.46300/91012.2021.15.2https://www.naun.org/main/NAUN/energyenvironment/2021/a042011-002(2021).pdfKendall, M. EU proposal for a directive on the establishment of a community framework for noise classification of on civil subsonic aircraft for the purposes of calculating noise charges, European Union, 2003 10.1177/002029400103400303Holding, J. M.: Aircraft noise monitoring: principles and practice, IMC measurement and Control, vol. 34, issue 3, pp. 72-76, 2001. 10.1007/s11063-012-9258-5Sánchez, L., Sánchez, L. A., Carbajal, J., and Rojo, A. Aircraft Classification and Acoustic Impact Estimation Based on Real-Time Take-off Noise Measurements. Neural Processing Letters, 1-21, 2012. 10.1016/j.eswa.2013.03.017Sánchez-Pérez, L. A., Sánchez-Fernández, L. P., Suárez-Guerra, S. and Carbajal-Hernández, J. J. Aircraft class identification based on take-off noise signal segmentation in time. Expert Systems with Applications, 40, 5148-5159, 2013. International Electrotech. Comm. (IEC): Standard IEC651: Sound Level Meters, 1979. Chu, W.: Speech Coding Algorithm: Foundation and Evolution of Standardized Coders, J. Wiley, 2003. Lochard - Airport Environment Management: EMU2100 Brochure, 2008. 10.4018/978-1-59904-132-2Perez-Meana, H. (ed.): Advances in Audio and Speech Signal Processing: Technologies and Applications, Idea Group Pub. 2007. International Electrotech. Comm. (IEC): Standard IEC1260: Octave Filters, 1995. 10.3403/30177581uAmerican National Standards Institute (ANSI): Standard S1.11-2004: Specification for Octave-Band and Fractional-Octave-Band Analog and Digital Filters, 2004. Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing. Englewood Cliffs, NJ: Prentice Hall, 1989. Pgs. 311-312. 10.1007/978-3-540-76725-1_86Sanchez, L. et al.: Noise pattern recognition of airplanes taking off: task for a monitoring system. Lecture Notes in Computer Science, vol. 4756, pp. 831-840, 2007. 10.1109/tau.1967.1161901Welch, P.D. "The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms." IEEE Trans. Audio Electroacoust. Vol. AU-15 (June 1967). Pgs. 70-73. Kryter KD. The effects of noise in man: Academic Press; 1985. Berglund B, Lindvall T, Schwela DH. Guidelines for community noise: World Health Organization (WHO); 1999. Harris C. Handbook of acoustical measurements and noise control: McGraw-Hill; 1995. Berglund B, Lindvall T, Schwela DH. Community noise: World Health Organization (WHO); 1995. Alves-Pereira M, Castelo Branco N. Vibroacoustic disease2004. Recuero M. Ingeniería acústica: Paraninfo; 1994. Ostrosky-Solís F. Toc toc, ¿hay alguien ahí?: Infored; 2001. 10.1260/147547308785082623Michaud DS, Fidell S, Pearsons K, Campbell KC, Keith SE. Review of field studies of aircraft noise-induced sleep disturbance. J Acoust Soc Am 2007;121(1):32-41. Knipschild P. V. Medical effects of aircraft noise: Community cardiovascular survey. Int Arch Occ Env Hea 1977;40(3):185-90. 10.5220/0003679101530162Sánchez, L., Sánchez, L. A, Ibarra, M. Aircraft Classification and Noise Map Estimation Based on Real-Time Measurements of Take-off Noise, Proceedings of NCTA 2011 International Conference on Neural Computation Theory and Applications, Paris, France. 24-26 October, 2011, pp.153-161. ISBN: 978-989-8425-84-3. 10.1007/978-3-540-76725-1_86Sanchez, L., Pogrebnyak, O., Oropeza, J. and Suárez, S. Noise pattern recognition of airplanes taking off: task for a monitoring system. Lecture Notes in Computer Science, vol. 4756, pp. 831-840, 2007.