Statistical analysis of sound level meter monitoring

Abstract

Long-term monitoring by means of sound level meters are one of the most common ways of analyzing indoor and outdoor sound environments by technicians. The equivalent sound level L eq and statistical levels L n -where n is the acoustical percentile of the statistical population -are the main noise descriptors used in the technical praxis. However, real-world scenarios are complex, and the mentioned metrics describe solely a general view of the monitored acoustic scene. Measurements show how long-term monitoring shape multimodal densities of sound pressure levels. Thus, clustering algorithms can provide deeper tools to perform statistical analyses on sound level meter monitoring. In the present work, the Gaussian Mixture Model (GMM) is used to analyze different synthetic scenarios based on real-world measurements. The comparison among the energetic and the statistical metrics used in the common praxis and the numerical features obtained via GMM highlights the ability of a deeper statistical approach to bring more insights to technicians to analyze active sound environments.