Generating and Structuring Perceptual Attributes for Dining-Space Soundscapes: A Preparatory Study for PCA Modeling

In soundscape studies, several models of perceived affective quality have been developed for specific indoor spaces to help characterize comfortable and satisfying environments. However, no model has been developed so far for dining spaces and the validity of existing models proposed for other indoor contexts, like residential buildings and offices, in dining spaces settings remains uncertain. Therefore, this sequential mixed-method study was conducted to establish a list of attributes that researchers may use to describe dining space soundscapes, which will help in developing an indoor soundscape model for dining spaces. A total of 505 potential attributes were identified and collected through multiple approaches including a Large Language Model-driven synthesis and a systematic review of literature related to dining space soundscapes. Subsequently, the original attributes were refined using qualitative analysis methods in several steps, resulting in a final set of 129 single-word attributes, clustered into 53 semantically coherent groups. This set of attributes will contribute to identifying the main perceptual dimensions of dining space soundscapes and constructing a principal components model through quantitative analyses, while the proposed methodology integrates an LLM-driven synthesis with iterative qualitative expert refinement and offers a more efficient and scalable alternative to conventional labor-intensive manual descriptor extraction for attribute collection, therefore supporting efficient and scalable attribute compilation.