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

Room acoustical parameters (RAPs), room geometrical parameters (RGPs) and instantaneous occupancy levels 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 blind estimation methods either fail to cover a broad range of real-world acoustic environments in the context of real background noise or estimate only a few RAPs and RGPs from noisy single-channel speech signals. In addition, they are limited in their ability to estimate the instantaneous occupancy level. In this paper, we propose BERP, a new universal approach to blindly estimate RAPs, RGPs, and occupancy levels. It consists of two modules: one for RAPs and RGPs and another for occupancy levels. For the former task, we use a shared room feature encoder that combines attention mechanisms with convolutional layers to learn common features across room parameters, and multiple separate parametric predictors for continuous estimation of each parameter in parallel. The combination of attention and convolutions enables the model to capture acoustic features both locally and globally from speech, yielding more robust and multitask generalizable common features. Separate predictors allow the model to independently optimize for each room parameter to reduce task learning conflict and improve per-task performance. This architecture enables universal and efficient estimation of room parameters while maintaining satisfactory performance. For occupancy level estimation, we reuse the identical encoder with a classification head, exploiting the encoder's strong sequencefeature extraction. To evaluate the effectiveness of the proposed approach, we compile a task-specific dataset from several publicly available datasets, including synthetic and real reverberant recordings. The results reveal that BERP achieves state-of-the-art (SOTA) performance and excellent adaptability to real-world scenarios. The code, datasets, and weights are available on GitHub.