The prediction of spatial auditory perception from acoustic differences between the real listening environment and virtual audio is of vital importance to make audio augmented reality applications valid. Therefore we investigate whether perceptual mismatch can be quantitatively predicted using descriptions of parametric differences and if such predictions can be generalized across different listening environments. This contribution examines the ability of statistical models to predict auditory perceptual differences based on acoustic parameters of auditory perception, focusing on envelopment and plausible fit. Acoustic parameters are utilized from measured binaural room impulse responses of seven rooms with diverse acoustic properties. These parameters are correlated with subjective data from listening tests from three different rooms. The correlation analysis approach using a generalized linear mixed model methodically shows that auditory perception can be predicted from acoustic parameters with a certain degree of room independence. This is especially true for the feature envelopment, while the prediction of plausible fit was more strongly linked to the specific characteristics of the listening room. Additionally, the study presents limitations, such as the small sample size and the number of validated models. The paper contributes to the field of room acoustics by proposing, applying, and analyzing a methodology for predicting auditory-perceptive differences. It also highlights the challenges of achieving room-independent predictions.
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