.png){kind=icon}
返回
Speech enhancement based on simple recurrent unit network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.apacoust.2019.107019.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Speech enhancement is a crucial and challenging task in many applications. A novel speech enhancement method based on the simple recurrent unit (SRU) is proposed in this paper. First, the log-power spectra of noisy and clean speeches are extracted. Then, the mapping relationship between noisy and clean speech spectra is learned by a multiple-layer stacked SRU network. Finally, the well-trained model is used to predict the corresponding clean speech spectra from the noisy speech spectra and the whole clean speech waveform can be recovered. Compared with the existing algorithms, DNN, LSTM and GRU, the proposed method achieves significant improvements at training speed and has capability to balance the performance and the training time. Experimental results demonstrate the validity and robustness of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Speech enhancement
Deep neural network
Simple recurrent unit
Power spectra
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
7.4K
被引数:
1.7W
机构
引用论文
A Comparison of the Effectiveness of Sodium Stibogluconate Monotherapy to Sodium Stibogluconate and Paromomycin Combination for the Treatment of Severe Post Kala Azar Dermal Leishmaniasis in South Sudan – A Retrospective Cohort Study.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Design and implementation of subspace-based speech enhancement under in-car noisy environments车内噪声环境下基于子空间的语音增强设计与实现
PREAI
分享收藏A Pairwise Algorithm Using the Deep Stacking Network for Speech Separation and Pitch Estimation
PREAI
分享收藏
查看更多内容