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.
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