Clustering analyses to assess HVAC noise in real-world conditions

Abstract

The assessment of systems' noise during measurements is usually carried out in an empty state of the spaces under study. Long-term monitoring in real-world conditions can provide insights into the performance of systems during the day. Thus, it becomes useful to find methods able to separate coexisting sound sources in the same space. Clustering techniques can supply the lack of robust methods to perform the segregation. Previous works have shown reliable results about distinguishing human and mechanical noises through sound level meter long-term measurements. Sound pressure levels (SPLs) were post-processed via Gaussian Mixture Model and K-means clustering with a three-step algorithm. The first step is clustering validation and concerns the assessment of the optimal number of clusters among the candidate models. Then, SPLs were divided into the number of clusters obtained by the validation. Finally, statistical and metrical features were used to label the sound sources whether mechanical or human, depending on the algorithm that performed the clustering. Carrying out these algorithms during real-time conditions is possible to monitor the actual HVAC noise providing more accurate analyses. Further studies will focus on broadening the ability to analyze the mechanical noise into its components, such as continuous and discontinuous sources.