  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.
