Sound power level estimation - choice of the prior distribution

• Autorzy: Stępień Bartłomiej

Identyfikatory

DOI

10.21008/j.0860-6897.2023.1.10

• Języki publikacji: EN

Abstrakty

EN

Bayesian inference is one of the methods used to determine the sound power level of sound sources. This method requires knowledge of two probability distributions. The first is the sampling density, while the second is the prior distribution. In this study, the effect of the prior distribution on the sound power level estimation results was investigated. For this purpose, three prior distributions were used: 1) a normal distribution, 2) a distribution determined using the kernel density estimator, 3) a uniform distribution. The sound power level results determined by the engineering method were used to illustrate the proposed solutions and carry out the analysis. The results of the experiment were compared with the results of the sound power level determined using the precision method in the hemi-anechoic room according to ISO 3745:2012. The statistical inference has been carried out based on results of non-parametric statistical tests at the significance level α = 0.05.

Słowa kluczowe

EN

sound power level; engineering methods; Bayesian statistics; prior distribution

PL

poziom mocy akustycznej; metody inżynierskie; statystyka bayesowska; rozkład a priori

• Wydawca: Poznan University of Technology. Institute of Applied Mechanics
• Czasopismo: Vibrations in Physical Systems
• Rocznik: 2023
• Tom: Vol. 34, nr 1
• Strony: art. no. 2023110
• Opis fizyczny: Bibliogr. 12 poz., wykr.

Twórcy

autor

Stępień Bartłomiej

• AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics, Department of Mechanics and Vibroacoustics, al. A. Mickiewicza 30, 30-059 Kraków, Poland

• https://orcid.org/0000-0002-6578-9693

Bibliografia

• 1. ISO 3745:2012; Acoustics. Determination of sound power levels and sound energy levels of noise sources using sound pressure. Precision methods for anechoic rooms and hemi-anechoic rooms.
• 2. ISO 3744:2010; Acoustics. Determination of sound power levels and sound energy levels of noise sources using sound pressure. Engineering methods for an essentially free field over a reflecting plane.
• 3. ISO 3746:2010; Acoustics. Determination of sound power levels and sound energy levels of noise sources using sound pressure. Survey method using an enveloping measurement surface over a reflecting plane.
• 4. B. Stępień; The use of the bootstrap method for determining sound power level; Proceedings of the 11th European Congress and Exposition on Noise Control Engineering, Heraklion, Crete-Greece, May 27-31, 2018, 593-598
• 5. B. Stępień, T. Wszołek; Bayesian estimation of sound power level; Proceedings of the 48th international Congress and Exhibition on Noise Control Engineering: noise control for a better environment, Madrid, Spain, June 16-19, 2019; Antonio Calvo-Manzano, et al., Eds.; Spanish Acoustical Society, Madrid, Spain, 2019, 1-8
• 6. J. V. Candy; Bayesian signal processing: classical, modern, and particle filtering methods; John Wiley & Sons, Inc.: Hoboken, USA, 2009
• 7. S. Efromovich; Nonparametric curve estimation: methods, theory, and applications; Springer-Verlag: New York, USA, 1999
• 8. B. Stępień; Confidence intervals for the long-term noise indicators using the kernel density estimator; Arch. Acoust., 2016, 41(3), 517-525; DOI: 10.1515/aoa-2016-0050
• 9. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, E. Teller; Equations of state calculations by fast computing machines; J. Chem. Phys., 1953, 21(6), 1087-1092
• 10. W. K. Hastings; Monte Carlo sampling methods using Markov chains and their application; Biometrika, 1970, 57(1), 97-109
• 11. D. Gamerman, H. F. Lopes; Markov chain Monte Carlo: stochastic simulation for Bayesian inference; Chapman & Hall/CRC: Boca Raton, USA, 2006
• 12. Sound power source - type 4205. Product data. From https://www.bksv.com/media/doc/bp0264.pdf (accessed on 2022.09.26)

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).