Evaluation of Psychoacoustic Machine Learning Assessment Method for Predicting Occupant Well-Being

In modern buildings, the air-conditioned indoor environment is vital for occupant productivity and well-being, yet fan noise and airflow turbulence can significantly compromise these benefits. Human–environmental interactions are complex processes that traditional energy-based acoustic metrics are often insufficient to model. Therefore, this study aims to advance the multidimensional sound quality assessment framework for building acoustics. Three methods, the conventional regression approach (CRA), general prediction model (GPM), and psychoacoustic machine learning (PML) assessment methods, were evaluated for predicting three perceptual dimensions (Evaluation, Potency, Activity; EPA) and negative noise impacts on occupant well-being (O1: Discomfortable, O2: Annoying, O3: Stressful, and O4: Unacceptable). Based on 432 multidimensional sound quality assessments across four general types of air-conditioned built environments, the PML achieved the best goodness-of-fit for the EPA-score perdition (adjusted R2 = 0.61) compared to CRA (0.32) and GPM (0.15) and effectively predicted all negative noise impacts (adjusted R2 = 0.53–0.61). The PML assessment method offers a smart and reliable solution for sound quality and well-being prediction through psychoacoustic heatmaps encoding time-varying psychoacoustic features in 227 × 227 pixels from 30 s soundtracks of the built environment for sustainable building design.