FADI-AEC: Fast Score Based Diffusion Model Guided by Far-end Signal for
  Acoustic Echo Cancellation
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.
659e1c93da61c174cc1c2d1a
Yang Liu
659e1c93da61c174cc1c2d1b
Li Wan
659e1c93da61c174cc1c2d1c
Yun Li
659e1c93da61c174cc1c2d1d
Yiteng Huang
659e1c93da61c174cc1c2d1e
Ming Sun
659e1c93da61c174cc1c2d1f
James Luan
659e1c93da61c174cc1c2d20
Yangyang Shi
659e1c93da61c174cc1c2d21
Xin Lei
FADI-AEC: Fast Score Based Diffusion Model Guided by Far-end Signal for
  Acoustic Echo Cancellation
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.
DI-AEC and FADI-AEC are diffusion-based methods for AEC that enhance processing efficiency and accuracy using far-end signals.
diffusion models stochastic regeneration Acoustic Echo Cancellation (AEC) score-based diffusion edge devices noise generation far-end signals near-end signals ICASSP2023 Microsoft deep echo cancellation challenge