Matching Reverberant Speech Through Learned Acoustic Embeddings and Feedback Delay Networks (2510.23158v1)

Abstract: Reverberation conveys critical acoustic cues about the environment, supporting spatial awareness and immersion. For auditory augmented reality (AAR) systems, generating perceptually plausible reverberation in real time remains a key challenge, especially when explicit acoustic measurements are unavailable. We address this by formulating blind estimation of artificial reverberation parameters as a reverberant signal matching task, leveraging a learned room-acoustic prior. Furthermore, we propose a feedback delay network (FDN) structure that reproduces both frequency-dependent decay times and the direct-to-reverberation ratio of a target space. Experimental evaluation against a leading automatic FDN tuning method demonstrates improvements in estimated room-acoustic parameters and perceptual plausibility of artificial reverberant speech. These results highlight the potential of our approach for efficient, perceptually consistent reverberation rendering in AAR applications.