融合GMM及SVM的特定音频事件高精度识别方法

High-Precision Specific Audio Event Recognition Method Combining SVM and GMM

  • 摘要: 针对特定音频事件识别中持续时间特别短的音频事件漏检概率高、识别速度较慢的问题,提出一种融合高斯混合模型(GMM)及支持向量机(SVM)的特定音频事件识别算法. 该方法利用GMM的统计分布描述能力和SVM的推广泛化能力,将GMM和SVM分别识别的结果进行融合处理,以手枪、步枪、机关枪等10类以上枪声为实验数据,无需针对每种枪声生成相应的识别模板,仅需训练生成2个识别模板. 实验结果表明,识别准确率达到92.71%. 该方法模板数量少,不需要多次训练,算法复杂度较低,不仅便于应用而且可大幅提升识别效率.

     

    Abstract: There are several problems such as high time consumption and low recognition accuracy on the short duration audio events. In this paper, an audio event recognition method that combined GMM and SVM was put forward. The method used the statistical distribution description of GMM and the promote generalization ability of SVM, and used the respective result of the recognition of GMM and SVM fusion processing, ten-type gunshots, such as handguns, rifles, machine guns etc. were used as the experimental data. Compared with the ordinary methods, which have to train 10 specific templates from each gunshot type, the proposed method just needed 2 templates to fulfill the recognition task. Experimental results show that the proposed method yields an accuracy of 92.71%. Furthermore, because much fewer templates and training processes are required, this method is easy to implement and improves the efficiency significantly with a low algorithm complexity.

     

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