DARAS: Dynamic Audio-Room Acoustic Synthesis for Blind Room Impulse Response Estimation

TL;DR

DARAS is a deep learning framework that accurately estimates room impulse responses from monaural speech signals, improving acoustic modeling for AR/VR applications through innovative feature extraction, parameter estimation, and adaptive synthesis.

Contribution

The paper introduces DARAS, a novel deep learning model combining a dedicated encoder, self-supervised parameter estimation, and adaptive acoustic tuning for blind RIR estimation.

Findings

DARAS outperforms existing models in subjective listening tests.

The system effectively captures room acoustic parameters from monaural speech.

Experimental results show improved realism in synthesized RIRs.

Abstract

Room Impulse Responses (RIRs) accurately characterize acoustic properties of indoor environments and play a crucial role in applications such as speech enhancement, speech recognition, and audio rendering in augmented reality (AR) and virtual reality (VR). Existing blind estimation methods struggle to achieve practical accuracy. To overcome this challenge, we propose the dynamic audio-room acoustic synthesis (DARAS) model, a novel deep learning framework that is explicitly designed for blind RIR estimation from monaural reverberant speech signals. First, a dedicated deep audio encoder effectively extracts relevant nonlinear latent space features. Second, the Mamba-based self-supervised blind room parameter estimation (MASS-BRPE) module, utilizing the efficient Mamba state space model (SSM), accurately estimates key room acoustic parameters and features. Third, the system incorporates a hybrid-path cross-attention feature fusion module, enhancing deep integration between audio and room acoustic features. Finally, our proposed dynamic acoustic tuning (DAT) decoder adaptively segments early reflections and late reverberation to improve the realism of synthesized RIRs. Experimental results, including a MUSHRA-based subjective listening study, demonstrate that DARAS substantially outperforms existing baseline models, providing a robust and effective solution for practical blind RIR estimation in real-world acoustic environments.