Classification of urban park soundscapes through perceptions of the acoustical environments

Abstract

The aim of this study is to classify the soundscape characteristics in urban parks based on perceptions of acoustic environments and to examine useful acoustic indicators to identify soundscape classifications. Both acoustic measurements and subjective evaluations of the soundscape were conducted at various locations in three urban parks. Using hierarchical cluster analysis (HCA), the soundscape perceptions in urban parks were classified into three clusters characterized by the dominance of sound sources, such as traffic noise, natural and human-made sounds. Both discriminant function analysis (DFA) and artificial neural network (ANN) analysis were performed using various acoustic parameters to discern which of them best differentiate the soundscape classifications in urban parks. It was found that the indicators representing sound strength (L-Aeq), perceived pitch sensation (tau(1)) and strength of the pitch components (Phi(1)) of sound scenes could effectively identify the clusters. In particular, autocorrelation function (ACF) parameters tau(1) and Phi(1) were significantly correlated with identification of traffic noise and human sounds. It was revealed that sounds caused by various human activities in parks play an important role to influence eventfulness of soundscape perception. Soundscape perceptions were also closely correlated with esthetic quality, simplicity and sense of enclosure of landscape. Despite the fact that the results might be limited to the local conditions in Seoul, the findings could provide useful information for designing appropriate soundscapes in urban parks.