Acoustic augmented realities aim to incorporate virtual elements on top of real acoustic environments. A key question is how accurately virtual acoustics must match real acoustics to enable perceptually seamless integration. In six-degrees-of-freedom scenarios, where listeners can explore spaces, intraroom acoustic variation is of particular interest. This paper presents a method for modeling the perceptibility of acoustic changes in an acoustic augmented realities context using a generalized linear mixed model based on differences in room acoustic parameters computed from pairs of room impulse responses. An online listening test based on extensive acoustic measurements investigated perceptible differences between three acoustic configurations of the same room using identical source-receiver arrangements. A total of 4,753 ratings from 120 participants in an ABX-like format (a method where participants identify whether a presented stimulus X matches stimulus A or B) were used to train the generalized linear mixed model. The resulting model shows good predictive performance (Brier score: 0.19; AUC: 0.67) based on a fivefold cross-validation with 100 repetitions, although improvements are possible. However, the perceptibility of acoustic changes can vary significantly between dynamic and static listening scenarios. The paper discusses evaluation strategies for these aspects, examines the model’s generalizability, highlights current limitations, and outlines directions for future improvement.
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