Deep learning for minimum mean-square error approaches to speech enhancement
介紹 目標是縮小MMSE和深度學習語音增強方法之間的差距，生成比最近基於masking和mapping的深度學習方法更高質量和可理解性分數的增強語音。 研究了深度學習方法可以爲上述MMSE方法提供的性能改進。每種MMSE方法都需要對帶噪語音頻譜成分進行先驗信噪比（SNR）估計。 這裏採用了深度學習的方法準確估計先驗信噪比。 在這項工作中，使用語音質量和可懂度的主客觀測量來評估使用深度學習的MMSE
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相關文章
- 1. 論文筆記：DEEP LEARNING FOR MONAURAL SPEECH SEPARATION
- 2. Speech Enhancement Generation Adversarial Network
- 3. 平安-Meta-learning for Multilingual Text-to-Speech
- 4. Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
- 5. A Deep Neural Network Approach To Speech Bandwidth Expansion
- 6. comments on"An Experimental Study on Speech Enhancement Based on Deep Neural Networks"(一)
- 7. Deep learning From Image to Sequence
- 8. AdamTechLouis's talk:Deep Learning approaches to understand Human Reasoning
- 9. MyDLNote-Enhancement: [2020] Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
- 10. Deep Learning for NLP Best Practices