The character of an urban acoustic environment does not arise from individual sounds in isolation, but from their complex interactions. Current source-focused approaches reduce acoustic environments to inventories of sounds, overlooking the relational patterns that shape them. Here, we introduce a graph-theoretical framework that shifts the focus from ‘what is present’ to ‘how it connects’, quantifying the co-occurrence structure of urban soundscapes. Based on six months of continuous monitoring across nine contrasting sites, we constructed temporal co-occurrence networks comprising 26 sound classes. In line with our first research question, the resulting networks revealed three recurrent archetypes, dominant, transitional, and mixed, distinguished by measures of clustering, modularity, and centrality. The dominant type is characterized by the prevalence of a single sound source, the mixed type by the superposition of many heterogeneous sources, and the transitional type represents an intermediate state between the two. Conceptually, these measures capture three complementary structural dimensions: integration (clustering), segregation (modularity), and dominance (centrality). To test whether these structural regimes align with human perception, we conducted 1006 in-situ surveys. Mixed soundscapes, characterized by high integration and diversity of sound sources, were consistently perceived as more pleasant than dominant soundscapes. Our findings demonstrate that perceived quality depends not simply on how many sources are present, but on how they interact and integrate within the acoustic network. This structural approach provides robust, transferable metrics for designing healthier and more resilient urban environments. • Urban acoustic environments were represented as sound co-occurrence networks. • Six months of recordings were temporally aligned with in-situ survey responses. • Three structural archetypes were identified: dominant, transitional, and mixed. • Greater clustering was associated with higher pleasantness, whereas modularity reduced it. • The framework provides scalable indicators for urban soundscape assessment.
Karges et al. (Tue,) studied this question.