Unsupervised analysis of background noise sources in active officesa)

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.

Automated summary

Background noise in open-plan offices affects workers' comfort and productivity, with human noise often neglected. This paper proposes methods based on machine-learning techniques to identify human and mechanical noise sources, validating their robustness and reliability. Algorithms successfully match expected shapes of human speech and mechanical noise tendencies.