title: FADI-AEC: Fast Score Based Diffusion Model Guided by Far-end Signal for
    Acoustic Echo Cancellation
summary: 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.
paper_authors._id: 659e1c93da61c174cc1c2d1a
paper_authors.name: Yang Liu
paper_authors._id: 659e1c93da61c174cc1c2d1b
paper_authors.name: Li Wan
paper_authors._id: 659e1c93da61c174cc1c2d1c
paper_authors.name: Yun Li
paper_authors._id: 659e1c93da61c174cc1c2d1d
paper_authors.name: Yiteng Huang
paper_authors._id: 659e1c93da61c174cc1c2d1e
paper_authors.name: Ming Sun
paper_authors._id: 659e1c93da61c174cc1c2d1f
paper_authors.name: James Luan
paper_authors._id: 659e1c93da61c174cc1c2d20
paper_authors.name: Yangyang Shi
paper_authors._id: 659e1c93da61c174cc1c2d21
paper_authors.name: Xin Lei
paper_title: FADI-AEC: Fast Score Based Diffusion Model Guided by Far-end Signal for
    Acoustic Echo Cancellation
paper_summary: 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.
paper_ai_keywords: diffusion models Acoustic Echo Cancellation (AEC) DI-AEC FADI-AEC score-based diffusion computational aims far-end signals near-end signals score model ICASSP2023 Microsoft deep echo cancellation challenge