Paper ID: 2405.04476

BERP: A Blind Estimator of Room Acoustic and Physical Parameters for Single-Channel Noisy Speech Signals

Lijun Wang, Yixian Lu, Ziyan Gao, Kai Li, Jianqiang Huang, Yuntao Kong, Shogo Okada

Room acoustic parameters (RAPs) and room physical parameters (RPPs) are essential metrics for parameterizing the room acoustical characteristics (RACs) of a sound field around a listener's local environment, offering comprehensive indications for various applications. Current RAP and RPP estimation methods either fall short of covering broad real-world acoustic environments in the context of real background noise or lack universal frameworks for blindly estimating RAPs and RPPs from noisy single-channel speech signals, particularly sound source distances, direction of arrival (DOA) of sound sources, and occupancy levels. On the other hand, in this paper, we propose a new universal blind estimation framework called the blind estimator of the room acoustical and physical parameters (BERP), by introducing a new stochastic room impulse response (RIR) model, namely the sparse stochastic impulse response (SSIR) model, and endowing the BERP with a unified encoder and multiple separate predictors to estimate the RPPs and the parameters SSIR in parallel. This estimation framework enables computationally efficient and universal estimation of room parameters using only noisy single-channel speech signals. Finally, all RAPs can be simultaneously derived from RIRs synthesized from the SSIR model with estimated parameters. To evaluate the effectiveness of the proposed BERP and SSIR models, we compile a task-specific dataset from several publicly available datasets. The results reveal that the BERP achieves state-of-the-art (SOTA) performance. In addition, the evaluation results for the SSIR RIR model also demonstrated its efficacy. The code is available on GitHub.

Submitted: May 7, 2024