FADI-AEC：基于快速评分的远端信号引导扩散模型用于声学回声消除

摘要

尽管扩散模型在语音增强中具有潜力，但它们在声学回声消除（AEC）中的应用受到限制。在本文中，我们提出了DI-AEC，开创了一种基于扩散的随机再生方法，专门用于AEC。此外，我们提出了FADI-AEC，一种快速基于得分的扩散AEC框架，以节省计算需求，使其适用于边缘设备。它通过在每帧中运行得分模型一次，实现了处理效率的显著提升。除此之外，我们引入了一种新颖的噪声生成技术，利用远端信号，将远端和近端信号结合起来，以提高得分模型的准确性。我们在ICASSP2023微软深度回声消除挑战评估数据集上测试了我们提出的方法，在那里我们的方法优于一些端到端方法和其他基于扩散的回声消除方法。

English

Despite the potential of diffusion models in speech enhancement, their deployment in Acoustic Echo Cancellation (AEC) has been restricted. In this paper, we propose DI-AEC, pioneering a diffusion-based stochastic regeneration approach dedicated to AEC. Further, we propose FADI-AEC, fast score-based diffusion AEC framework to save computational demands, making it favorable for edge devices. It stands out by running the score model once per frame, achieving a significant surge in processing efficiency. Apart from that, we introduce a novel noise generation technique where far-end signals are utilized, incorporating both far-end and near-end signals to refine the score model's accuracy. We test our proposed method on the ICASSP2023 Microsoft deep echo cancellation challenge evaluation dataset, where our method outperforms some of the end-to-end methods and other diffusion based echo cancellation methods.

FADI-AEC：基于快速评分的远端信号引导扩散模型用于声学回声消除

FADI-AEC: Fast Score Based Diffusion Model Guided by Far-end Signal for Acoustic Echo Cancellation

摘要

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