Device-Centric Room Impulse Response Augmentation Evaluated on Room Geometry Inference

Because suitable real-world room impulse responses (RIRs) are scarce for many use cases, synthetic RIRs are indispensable to data-driven audio applications; however, any mismatch between them and real-world RIRs can cause significant performance degradation due to domain shift. This study proposes a device-centric RIR augmentation method that reduces this mismatch by incorporating the acoustic characteristics of transducers from a specific audio device along with a reverberation model to generate synthetic RIRs. As an application example, we evaluate the effect of the proposed augmentation technique on the problem of room geometry inference (RGI) using a smart speaker. A neural network is trained with and without augmented RIRs and tested with measured RIRs. Comparing the performance of both training scenarios indicates a reduced domain shift between augmented synthetic RIRs and real-world measurements. The results show a considerable improvement in the estimation accuracy when augmented RIRs are used for training.