Acoustic augmented realities aim to fuse real and virtual acoustic environments perceptually. The fundamental question is how accurately the virtual acoustics needs to match the real acoustics. In 6DoF scenarios, listeners can explore spaces and thus intra-room acoustic variations become of interest. This contribution presents an approach to predict the perceptibility of room acoustic variations. By utilizing an autonomous mobile robot platform dense room acoustic measurements were collected. Based on this data, listening tests were conducted with the sole purpose of matching the perceptual ratings with the room acoustic difference between positions within the same room. The perceptual ratings in the form of binary decisions (the difference is perceptible or not) and attribute ratings describing these differences are fed into a generalized mixed-effects model together with pre-calculated differences of room acoustic parameters. The results show, that we can create promising models based on the available data. However, the perceptibility in walking scenarios may differ significantly from static comparisons between positions. An approach to assess such cases is presented for discussion. Furthermore, we discuss the generalizability of the derived models, future improvements, and limitations.
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