Segan : Speech Enhancement Generative Adversarial Network
語音加強生成對抗網絡算法 目前的語音加強技術是在頻譜域上進行操做和/或利用一些更高級的特徵。它們中的大多數處理有限數量的噪聲條件，並依賴一階統計。爲了不這些問題，因爲可以從大型示例集中學習複雜的函數，深層網絡正愈來愈多地被使用。在這項工做中，咱們建議使用生成對抗網絡的語音加強。與目前的技術相比，咱們在波形級別操做，端到端地訓練模型，並將28個揚聲器和40個不一樣的噪聲條件合併到同一模型中，這樣模型
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相關文章
- 1. Speech Enhancement Generation Adversarial Network
- 2. 論文翻譯：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 與 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. [論文筆記] SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
- 10. Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks