《Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verifi》
摘要：在嘈杂环境中提高语音系统性能仍然是一项具有挑战性的任务，语音增强（SE）是解决该问题的有效技术之一。由于生成对抗网络（GAN）在各种图像处理任务中的有发展前景的实验结果，我们探索条件GAN（cGAN）对SE的潜力，特别是，我们利用Isola提出的图像处理框架[1] ]学习从嘈杂语音的谱图到增强对应物的映射。SEcGAN由两个以对抗方式训练的网络组成：一个试图增强输入噪声频谱图的生成器，以及一
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相关文章
- 1. Segan : Speech Enhancement Generative Adversarial Network
- 2. Speech Enhancement Generation Adversarial Network
- 3. Conditional Generative Adversarial Networks（CGAN）笔记
- 4. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 5. Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow
- 6. Conditional Generative Adversarial Nets
- 7. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization
- 8. Generative Adversarial Networks for Hyperspectral Image Classification
- 9. Graphical Generative Adversarial Networks
- 10. Conditional Generative Adversarial Nets（CGAN）