Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230600115-5.doi: 10.11896/jsjkx.230600115

• Interdiscipline & Application • Previous Articles     Next Articles

Low-rank HOG Voice Detection Method for Short-wave Communication

BAI Jie1, TIAN Ruili2, REN Yifu1, YUAN Jianxia1   

  1. 1 The 54th Research Institute of CETC,Shijiazhuang 050081,China
    2 China United Network Communications Co.,Ltd.Hebei Branch,Shijiazhuang 050051,China
  • Published:2024-06-06
  • About author:BAI Jie,born in 1981,postgraduate.His main research interests include big data and artificial intelligence technology applications.
  • Supported by:
    Development Fund Project of Hebei Key Laboratory of Intelligent Information Perception and Processing(SXX22138X002).

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Abstract: The low accuracy of voice detection in noisy environment is an open challenge for short wave communication.The application of existing methods is limited,because it is difficult to reliably extract accurate and efficient voice features in the noise environment.To solve the above problem,a Low-rank histogram of oriented gradient(LHOG) voice detection method for short wave communication is proposed in this paper.Firstly,target audio source data is preprocessed to realize visual representation of voice information in noisy environment.Then,a low-rank structure is embedded in the HOG feature extractor to alleviate redundant information and reduce noise interference,so as to obtain accurate and efficient features.Finally,the common SVM classification model can be used to reliably distinguish voice from noise in noisy environment.The test results show that the accuracy of this method is 95.12%,the false positive rate is 0.96%,and false negative rate is 13.14%.Compared with the existing mainstream methods,the experiment shows that the average detection accuracy of this method is higher,and resource occupation is less.Therefore,this method can effectively improve the detection and control efficiency of short-wave communication.

Key words: Pattern recognition, Spectrogram, HOG, Low-rank structure, SVM

CLC Number: 

  • TP391.4
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