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  • 标题:Sudden Noise Reduction Based on GMM with Noise Power Estimation
  • 本地全文:下载
  • 作者:Nobuyuki Miyake ; Tetsuya Takiguchi ; Yasuo Ariki
  • 期刊名称:Journal of Software Engineering and Applications
  • 印刷版ISSN:1945-3116
  • 电子版ISSN:1945-3124
  • 出版年度:2010
  • 卷号:3
  • 期号:4
  • 页码:341-346
  • DOI:10.4236/jsea.2010.34039
  • 出版社:Scientific Research Publishing
  • 摘要:This paper describes a method for reducing sudden noise using noise detection and classification methods, and noise power estimation. Sudden noise detection and classification have been dealt with in our previous study. In this paper, GMM-based noise reduction is performed using the detection and classification results. As a result of classification, we can determine the kind of noise we are dealing with, but the power is unknown. In this paper, this problem is solved by combining an estimation of noise power with the noise reduction method. In our experiments, the proposed method achieved good performance for recognition of utterances overlapped by sudden noises.
  • 关键词:Sudden Noise; Model-Based Noise Reduction; Speech Recognition
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