  Room acoustic parameters (RAPs) and room physical parameters ( RPPs) are
essential metrics for parameterizing the room acoustical characteristics (RAC)
of a sound field around a listener's local environment, offering comprehensive
indications for various applications. The current RAPs and RPPs 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 novel
universal blind estimation framework called the blind estimator of 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 RPPs and SSIR parameters in parallel. This
estimation framework enables the computationally efficient and universal
estimation of room parameters by solely using noisy single-channel speech
signals. Finally, all the RAPs can be simultaneously derived from the RIRs
synthesized from SSIR model with the 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. Moreover, the evaluation
results pertaining to the SSIR RIR model also demonstrated its efficacy. The
code is available on GitHub.
