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

TL;DR

BERP is a novel deep learning framework that accurately estimates room acoustical parameters, geometrical parameters, and occupancy levels from single-channel noisy speech, outperforming existing methods in real-world scenarios.

Contribution

The paper introduces BERP, a unified neural network model combining attention and convolutional layers for robust, multitask estimation of multiple room parameters from noisy speech signals.

Findings

BERP achieves state-of-the-art accuracy in estimating room parameters.

The model demonstrates high robustness in real-world noisy environments.

Separate predictors improve per-task estimation performance.

Abstract

Room acoustical parameters (RAPs), room geometrical parameters (RGPs) and instantaneous occupancy level 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 a new universal blind estimation framework called the blind estimator of room parameters (BERP) to estimate RAPs, RGPs and occupancy level via a unified methodology. It consists of two modules: a unified 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 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 estimation framework enables universal and efficient estimation of room parameters while maintaining satisfactory performance. To evaluate the effectiveness of the proposed framework, 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 and weights are available on GitHub.