Classification and ecological relevance of soundscapes in urban informal settlements - Genie M. Fleming; Moataz M. ElQadi; Ruzka R. Taruc; Autiko Tela; Grant A. Duffy; Emma E. Ramsay; Peter A. Faber; Steven L. Chown (People and Nature, 2023)
Abstract Sound exerts wide‐ranging influence on humans. The quality of that influence depends on the sound source and context in which it is perceived, but nature sounds are generally preferred by people and associated with health and well‐being benefits. In many environments, sounds are highly mixed giving rise to a multi‐source ‘soundscape’ that may vary through days or seasons. The complex and dynamic nature of soundscapes makes them challenging to quantify or classify to rigorously compare them and their contributing sources quantitatively through space and time. We address this challenge by developing an analytical procedure resulting in a generalized soundscape classification framework that (i) elucidates dominant sound sources (e.g. biophony vs. anthrophony) and (ii) can be used to improve our understanding of spatial and temporal variation in soundscapes across different contexts. We also address a knowledge gap in urban sound research by describing the soundscapes of urban informal settlements in Fiji and Indonesia. Despite the growing emphasis on improving the physical design and quality of life in urban informal settlements, little is known about soundscapes in these settings or their relationship to human health and well‐being. We identified seven soundscape classes representing relative dominance by (i) sustained geophony, biophony dominated by (ii) insect stridulation or (iii) bird song, anthrophony dominated by (iv) machines, (v) vehicles, (vi) human voices or (vii) a mixture of the former. These classes were applicable in both Indonesia and Fiji but differed in their prevalence between the countries, times of day and seasons in expected ways. Future automatic sorting of new sound data into this classification framework is provided by a supervised classification model that attained an overall testing accuracy of 94% and Cohen's kappa of 0.93. Our procedure yields broadly applicable, informative soundscape classes indicative of dominant sound sources, including natural sounds, that are known to have different effects on human health. Therefore, our soundscape classification framework can be used in conjunction with health, well‐being, or economic data, to aid the development, assessment and scaling of sustainable design solutions for liveable cities and especially for improving urban informal settlement environments. Read the free Plain Language Summary for this article on the Journal blog.