Speech enhancement based on noise classification and deep neural network

Author:

Wang Wenbo1,Liu Houguang1,Yang Jianhua1,Cao Guohua1,Hua Chunli1

Affiliation:

1. School of Mechatronic Engineering, China University of Mining and Technology, Da Xue Road No. 1, Xuzhou 221116, China

Abstract

Deep neural network (DNN) has recently been successfully adopted as a regression model in speech enhancement. Nonetheless, training machines to adapt different noise is a challenging task. Because every noise has its own characteristics which can be combined with speech utterance to give huge variation on which the model has to operate on. Thus, a joint framework combining noise classification (NC) and speech enhancement using DNN was proposed. We first determined the noise type of contaminated speech by the voice activity detection (VAD)-DNN and the NC-DNN. Then based on the noise classification results, the corresponding SE-DNN model was applied to enhance the contaminated speech. In addition, in order to make method simpler, the structure of different DNNs was similar and the features were the same. Experimental results show that the proposed method effectively improved the performance of speech enhancement in complex noise environments. Besides, the accuracy of classification had a great influence on speech enhancement.

Funder

National Natural Science Foundation of China

Top-notch Academic Programs Project of Jiangsu Higher Education Institutions

Fundamental Research Funds for the Central Universities

Publisher

World Scientific Pub Co Pte Lt

Subject

Condensed Matter Physics,Statistical and Nonlinear Physics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3