Segan : Speech Enhancement Generative Adversarial Network
语音加强生成对抗网络算法 目前的语音加强技术是在频谱域上进行操做和/或利用一些更高级的特征。它们中的大多数处理有限数量的噪声条件，并依赖一阶统计。为了不这些问题，因为可以从大型示例集中学习复杂的函数，深层网络正愈来愈多地被使用。在这项工做中，咱们建议使用生成对抗网络的语音加强。与目前的技术相比，咱们在波形级别操做，端到端地训练模型，并将28个扬声器和40个不一样的噪声条件合并到同一模型中，这样模型
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相关文章
- 1. Speech Enhancement Generation Adversarial Network
- 2. 论文翻译：Speech Super Resolution Generative Adversarial Network
- 3. Deep Generative Model2--GAN(Generative Adversarial Network)-cont
- 4. 《Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verifi》
- 5. Domain Adaptation 与 Generative Adversarial Network
- 6. Generative Adversarial Network 3 WGAN/ EBGAN
- 7. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORK
- 8. 【李宏毅2020 ML/DL】P75 Generative Adversarial Network | Conditional GAN
- 9. [论文笔记] SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
- 10. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks