Auralizing rooms with data-based dynamic binaural synthesis is an established approach in virtual acoustics. Generally measured binaural room impulse responses (BRIRs) are used to create a virtual acoustic environment (VAE) over headphones. Depending on the application, it is desirable to reduce the amount of data by decreasing the resolution of the BRIRs. For this reason a scalable parametric model for the synthesis of the binaural late reverberation part was developed and is presented. The model reduces the reverberation tail to three features only. Based on these features, BRIRs with synthetic reverberation are generated and compared to the corresponding measured impulse responses. The synthesis is evaluated perceptually in two listening experiments and differences between several settings of the algorithm as well as the performance for various rooms are examined. The results show only small perceptual differences between original and synthesis even with datasets heavily decreased in size.
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