This paper explores the outcome of training state-of-the-art dereverberation models with supervision settings ranging from weakly-supervised to virtually unsupervised, relying solely on reverberant signals and an acoustic model for training. Most of the existing deep learning approaches typically require paired dry and reverberant data, which are difficult to obtain in practice. We develop instead a sequential learning strategy motivated by a maximum-likelihood formulation of the dereverberation problem, wherein acoustic parameters and dry signals are estimated from reverberant inputs using deep neural networks, guided by a reverberation matching loss. Our most data-efficient variant requires only 100 reverberation-parameter-labeled samples to outperform an unsupervised baseline, demonstrating the effectiveness and practicality of the proposed method in low-resource scenarios.

翻译：本文探索了在监督设置下（从弱监督到近乎无监督）训练先进去混响模型的效果，仅依赖混响信号与声学模型进行训练。现有深度学习方法通常需要配对的无混响与混响数据，但实际中难以获取。我们则基于去混响问题的最大似然估计框架，提出一种序贯学习策略，通过深度神经网络从混响输入中估计声学参数与无混响信号，并辅以混响匹配损失进行引导。我们最高效的数据变体仅需100个带混响参数标注的样本即可超越无监督基线，证明了该方法在低资源场景下的有效性与实用性。