Inside open-plan offices, background noise affects the workers' comfort, influencing their productivity. Recent approaches identify three main source categories: mechanical sources (air conditioning equipment, office devices, etc.), outdoor traffic noise, and human sources (speech). Whereas the first two groups are taken into account by technical specifications, human noise is still often neglected. The present paper proposes two procedures, based on machine-learning techniques, to identify the human and mechanical noise sources during working hours. Two unsupervised clustering methods, specifically the Gaussian mixture model and K-means clustering, were used to separate the recorded sound pressure levels that were recorded while finding the candidate models. Thus, the clustering validation was used to find the number of sound sources within the office and, then, statistical and metrical features were used to label the sources. The results were compared with the common parameters used in noise monitoring in offices, i.e., the equivalent continuous and 90th percentile levels. The spectra obtained by the two algorithms match with the expected shapes of human speech and mechanical noise tendencies. The outcomes validate the robustness and reliability of these procedures.

Unsupervised analysis of background noise sources in active offices

De Salvio D.;D'Orazio D.
;
Garai M.
2021

Abstract

Inside open-plan offices, background noise affects the workers' comfort, influencing their productivity. Recent approaches identify three main source categories: mechanical sources (air conditioning equipment, office devices, etc.), outdoor traffic noise, and human sources (speech). Whereas the first two groups are taken into account by technical specifications, human noise is still often neglected. The present paper proposes two procedures, based on machine-learning techniques, to identify the human and mechanical noise sources during working hours. Two unsupervised clustering methods, specifically the Gaussian mixture model and K-means clustering, were used to separate the recorded sound pressure levels that were recorded while finding the candidate models. Thus, the clustering validation was used to find the number of sound sources within the office and, then, statistical and metrical features were used to label the sources. The results were compared with the common parameters used in noise monitoring in offices, i.e., the equivalent continuous and 90th percentile levels. The spectra obtained by the two algorithms match with the expected shapes of human speech and mechanical noise tendencies. The outcomes validate the robustness and reliability of these procedures.
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/835569
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 3
social impact