Speaker
Description
The correlation between acoustic indices (Bradfer-Lawrence et al. 2025; Dröge et al. 2024) and human responses—both subjective assessments and physiological responses—remains an understudied area in environmental soundscape research. While some recent studies (Durbridge and Murphy 2023; Qin et al. 2025; Uebel et al. 2025) shows subjective-physiological correlations, no study has integrated acoustic indices with both to predict responses across contexts. Preliminary work by (Rey Gozalo et al. 2015) correlated acoustic indices (e.g., LAeq) with subjective assessments, while (Versümer et al. 2025) modeled soundscape subjective assessments using machine learning on datasets like HSDD, ARAUS, and ISD. However, neither study included physiological responses or diverse environments. Currently, no standardized methodology exists to model acoustic indices alongside subjective and physiological data, limiting cross-study comparisons and model validation.The current research work presents a systematic review of available datasets that include subjective ratings and/or physiological measurements. The goal is to identify gaps in annotation standards and data compatibility to outline opportunities for future modeling efforts.Key aspects include:Data collection/validation (e.g., ISO 12913-2 compliance, sensor protocols).Annotation structure (e.g., aggregation level, inter-rater reliability).Challenges in aligning heterogeneous data (e.g., recording conditions, metadata).This review underscores the need for standardized data collection protocols and integrated datasets to enable robust predictive modeling of human responses to soundscapes. Future work aim to ultimately developing a model that predicts both subjective and physiological responses from acoustic indices.