Acoustical comfort inside open-plan offices is necessary for optimal work performance. In fact, it is well known that productivity is closely related to the acoustic conditions of the working environment. Noise inside offices is basically due to two kinds of sources: mechanical sources (HVAC devices, office equipment like printers, phones, etc.) and human activities (human activity noise). The combined eect of these noise sources may play a key role in privacy metrics, e.g. the STI and its spatial decay. Therefore some technique is needed to identify and separate the contribution of each kind of source. The data for the present work are a set of short-Leq values acquired over long-term sound pressure levels recordings. Noise sources are identified using a two-step statistical technique: at first the blind Gaussian Mixture Model (GMM) is used to segment the sources, then each source is classied using a customary statistical analysis. It is shown that the probability distribution of each source can be identified, conferring a different sound pressure level to each one. In order to investigate the dynamic behaviour of privacy criteria, these analyses are carried out for each octave band frequency.

A statistical analysis of noise sources in open plan offices

D’Orazio D.;Rossi E.
;
De Salvio D.;Garai M.
2019

Abstract

Acoustical comfort inside open-plan offices is necessary for optimal work performance. In fact, it is well known that productivity is closely related to the acoustic conditions of the working environment. Noise inside offices is basically due to two kinds of sources: mechanical sources (HVAC devices, office equipment like printers, phones, etc.) and human activities (human activity noise). The combined eect of these noise sources may play a key role in privacy metrics, e.g. the STI and its spatial decay. Therefore some technique is needed to identify and separate the contribution of each kind of source. The data for the present work are a set of short-Leq values acquired over long-term sound pressure levels recordings. Noise sources are identified using a two-step statistical technique: at first the blind Gaussian Mixture Model (GMM) is used to segment the sources, then each source is classied using a customary statistical analysis. It is shown that the probability distribution of each source can be identified, conferring a different sound pressure level to each one. In order to investigate the dynamic behaviour of privacy criteria, these analyses are carried out for each octave band frequency.
Proc. 23rd International Conference on Acoustics integrating 4th EAA Euroregio 2019 - ICA 2019
2460
2467
D’Orazio D., Rossi E., De Salvio D., Garai M.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/701282
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