FADI-AEC：由遠端信號引導的快速基於分數的擴散模型，用於聲學回音消除。

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

January 8, 2024

作者: Yang Liu, Li Wan, Yun Li, Yiteng Huang, Ming Sun, James Luan, Yangyang Shi, Xin Lei

cs.AI

摘要

儘管擴散模型在語音增強方面具有潛力，但它們在聲學回音消除（AEC）中的應用受到限制。在本文中，我們提出了DI-AEC，開創了一種基於擴散的隨機再生方法，專門用於AEC。此外，我們提出了FADI-AEC，一種快速基於分數的擴散AEC框架，以節省計算需求，使其適用於邊緣設備。它的獨特之處在於每幀運行一次分數模型，實現了處理效率的顯著提升。除此之外，我們引入了一種新型噪聲生成技術，其中利用遠端信號，將遠端信號和近端信號結合起來，以提高分數模型的準確性。我們在ICASSP2023 Microsoft深度回音消除挑戰評估數據集上測試了我們提出的方法，在那裡我們的方法優於一些端到端方法和其他基於擴散的回音消除方法。

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.