SS-BRPE: Self-Supervised Blind Room Parameter Estimation Using Attention Mechanisms

TL;DR

This paper introduces SS-BRPE, a self-supervised attention-based system for estimating room acoustic parameters from noisy speech, reducing reliance on labeled data and outperforming existing methods.

Contribution

The paper presents a novel self-supervised learning approach combined with attention mechanisms for blind room parameter estimation, enhancing accuracy and data efficiency.

Findings

Outperforms state-of-the-art methods in room parameter estimation

Maintains high accuracy with limited labeled data

Uses unlabeled data for effective pretraining

Abstract

In recent years, dynamic parameterization of acoustic environments has garnered attention in audio processing. This focus includes room volume and reverberation time (RT60), which define local acoustics independent of sound source and receiver orientation. Previous studies show that purely attention-based models can achieve advanced results in room parameter estimation. However, their success relies on supervised pretrainings that require a large amount of labeled true values for room parameters and complex training pipelines. In light of this, we propose a novel Self-Supervised Blind Room Parameter Estimation (SS-BRPE) system. This system combines a purely attention-based model with self-supervised learning to estimate room acoustic parameters, from single-channel noisy speech signals. By utilizing unlabeled audio data for pretraining, the proposed system significantly reduces dependencies on costly labeled datasets. Our model also incorporates dynamic feature augmentation during fine-tuning to enhance adaptability and generalizability. Experimental results demonstrate that the SS-BRPE system not only achieves more superior performance in estimating room parameters than state-of-the-art (SOTA) methods but also effectively maintains high accuracy under conditions with limited labeled data. Code available at https://github.com/bjut-chunxiwang/SS-BRPE.