用自监督学习估计房间声学参数，少依赖标注数据却更准。
SS-BRPE: Self-Supervised Blind Room Parameter Estimation Using Attention Mechanisms
- 纯注意力模型结合自监督预训练，从单通道噪声语音中估计房间参数。
- 在仅有少量标注数据时仍保持高精度，优于现有最先进方法。
- 适合音频处理、智能语音系统等需低成本标注的场景。
近年来，动态声学环境建模在音频处理中受到关注，其中房间体积和混响时间（RT60）是独立于声源与接收器方向的局部声学特征。已有研究表明，纯注意力模型在房间参数估计任务中表现优异，但其性能依赖大规模带标签数据和复杂的训练流程。为此，本文提出一种新型自监督盲房间参数估计（SS-BRPE）系统，结合纯注意力模型与自监督学习，从单通道噪声语音中估计房间声学参数。通过利用无标签音频数据进行预训练，显著降低对昂贵标注数据的依赖。模型在微调阶段引入动态特征增强，提升适应性与泛化能力。实验结果表明，该系统不仅在房间参数估计上超越现有最先进方法，且在标注数据有限条件下仍能保持高精度。代码已开源：https://github.com/bjut-chunxiwang/SS-BRPE。
原文摘要 · Abstract (English)
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.
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