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Related Experiment Videos

FAV-DenoiseNet: An Audio-Visual Speech Enhancement Framework Based on Conditional Flow Matching and Visual Encoding.

Xuan Fu1, Lulu Qin1, Weijing Liu1

  • 1School of Computer Science, Jilin Normal University, Siping 136000, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces FAV-DenoiseNet, a novel two-stage framework for audio-visual speech enhancement. It significantly reduces latency for real-time applications by using discriminative denoising and flow matching for efficient speech recovery.

Keywords:
audio–visual speech enhancementconditional flow matchingdiscriminative prior denoisingmulti-scale cross-modal attentionsingle-step inference

Related Experiment Videos

Area of Science:

  • Speech processing
  • Artificial intelligence
  • Signal processing

Background:

  • Diffusion-based methods offer high performance in audio-visual speech enhancement but suffer from high latency.
  • Real-time deployment of speech enhancement systems is hindered by computational costs.
  • Existing methods struggle to balance enhancement quality with inference speed.

Purpose of the Study:

  • To develop an efficient audio-visual speech enhancement framework (FAV-DenoiseNet) that overcomes the latency limitations of diffusion models.
  • To improve the quality of enhanced speech by effectively integrating visual cues.
  • To achieve real-time performance without compromising restoration accuracy.

Main Methods:

  • A two-stage framework combining discriminative prior denoising and conditional residual flow matching.
  • The first stage suppresses noise and provides a stable speech prior.
  • The second stage estimates the residual using single-step flow matching with multi-scale cross-modal attention and a residual-controlled fusion strategy.

Main Results:

  • FAV-DenoiseNet achieves state-of-the-art performance on benchmark datasets (VoxCeleb2, GRID) with high PESQ, ESTOI, and SI-SDR scores.
  • The proposed method demonstrates a low real-time factor (RTF) of 0.086, enabling efficient inference.
  • The framework effectively balances speech enhancement quality, detail restoration, and real-time processing.

Conclusions:

  • FAV-DenoiseNet successfully addresses the latency and computational cost issues of diffusion-based speech enhancement.
  • The proposed two-stage approach with residual compensation and cross-modal attention provides superior audio-visual speech enhancement.
  • The framework offers a promising solution for real-time audio-visual speech enhancement applications.