Acoustic Comfort Index (ACI): A transparent multi-parameter model for indoor acoustic assessment

Current indoor acoustic models mainly use sound pressure level cutoffs or occupant surveys, missing an interpretable, multi-factor index that links objective metrics with perceived indoor acoustic conditions. This paper introduces a fuzzy-logic Acoustic Comfort Index (ACI) that integrates six inputs: sound pressure level, dominant frequency, emission pattern, masking condition, acoustic criticality, and noise variability, into a continuous 0–1 score (lower indicating greater comfort) with categorical interpretation. The framework is conceptually informed by the ISO/TS 12913 soundscape paradigm, emphasizing contextual and perceptual aspects without relying on survey-based inputs. A 2025-rule Mamdani system is generated through automated rule construction. Model benchmarking includes 1000 randomized cases and 16 representative indoor scenarios. Local sensitivity analysis quantifies variable influence, while a ridge-regularized GAM surrogate confirms expected monotonic behavior and yields strong hold-out agreement. PCA clustering identifies comfort regimes that support practical diagnostic use. Benchmarking against SPL-only and reduced models illustrates the added value of contextual and perceptual variables. A MATLAB ACI Calculator App enables practical application from direct inputs or audio recordings, with calibrated inputs used for absolute SPL estimation.