噪声环境下语音检测准确率偏低是短波通话面临的公开挑战.当前已有方法应用有限,其根源在于难以可靠地在噪音环境下提取准确且高效的语音特征.针对上述问题,提出了一个面向短波通信的低秩方向梯度直方图(Low-rank Histogram of Oriented Gradient,LHOG)话音检测方法.首先,对目标音频源数据进行预处理,实现噪声环境下语音信息的可视化表征;然后,在HOG特征提取器中嵌入低秩化结构,缓解特征中的冗余信息,并降低噪声干扰,从而获得准确且高效的特征;最后,通过常用的SVM分类模型便可在噪声环境中准确快速地区分话音和噪声.测试结果表明,该方法的准确率达到了95.12％,误报率仅为0.96％,漏报率为13.14％.与现有主流方法的对比实验证明,该方法话音检测准确率高,资源占用少,能够有效提高短波通信侦控效率.
Low-rank HOG Voice Detection Method for Short-wave Communication
The low accuracy of voice detection in noisy environment is an open challenge for short wave communication.The ap-plication 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 redun-dant information and reduce noise interference,so as to obtain accurate and efficient features.Finally,the common SVM classifica-tion 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 main-stream 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.
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