EffiFusion-GAN: Efficient Fusion Generative Adversarial Network for Speech Enhancement
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Abstract (arXiv)
We introduce EffiFusion-GAN (Efficient Fusion Generative Adversarial Network), a lightweight yet powerful model for speech enhancement. The model integrates depthwise separable convolutions within a multi-scale block to capture diverse acoustic features efficiently. An enhanced attention mechanism with dual normalization and residual refinement further improves training stability and convergence. Additionally, dynamic pruning is applied to reduce model size while maintaining performance, making the framework suitable for resource-constrained environments. Experimental evaluation on the public VoiceBank+DEMAND dataset shows that EffiFusion-GAN achieves a PESQ score of 3.45, outperforming existing models under the same parameter settings.
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References · 14
- 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
- 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
- 2020Real Time Speech Enhancement in the Waveform Domaincited by 186
- 2017SEGAN: Speech Enhancement Generative Adversarial Networkcited by 166
- 2021MetricGAN+: An Improved Version of MetricGAN for Speech Enhancementcited by 114
- 2019MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancementcited by 104
- 2023MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectracited by 52
- 2019PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Networkcited by 51
- 2022CMGAN: Conformer-based Metric GAN for Speech Enhancementcited by 45
- 2021DPT-FSNet: Dual-path Transformer Based Full-band and Sub-band Fusion Network for Speech Enhancementcited by 33
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