文章摘要
曾金芳,黄费贞,白冰,徐林涛.基于耳蜗谱图纹理特征的声音事件识别[J].声学技术,2020,39(1):69~75
基于耳蜗谱图纹理特征的声音事件识别
Sound event recognition based on texture features of cochleagram
投稿时间:2019-01-05  修订日期:2019-02-25
DOI:10.16300/j.cnki.1000-3630.2020.01.012
中文关键词: Gammatone滤波器组  耳蜗谱图  Curvelet变换  完全局部二值模式  支持向量机
英文关键词: Gammatone filter bank  cochleagram  Curvelet transform  completed local binary pattern  support vector machine
基金项目:湖南省自然科学基金项目(2018JJ3486)、湘潭大学校级科研项目(16XZX02)、湘潭大学博士启动项目(15QDZ28)
作者单位E-mail
曾金芳 湘潭大学物理与光电工程学院, 湖南湘潭 411105 zengjinfang@xtu.edu.cn 
黄费贞 湘潭大学物理与光电工程学院, 湖南湘潭 411105  
白冰 湘潭大学物理与光电工程学院, 湖南湘潭 411105  
徐林涛 湘潭大学物理与光电工程学院, 湖南湘潭 411105  
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中文摘要:
      针对在各种环境下声音事件的识别问题,提出了一种基于谱图纹理特征的声音事件识别方法。首先,将声音信号通过伽马通(Gammatone)滤波器组,使原始声音样本转化为灰度耳蜗谱图;然后,对谱图进行曲波(Curvelet)变换,得到不同尺度、不同方向的Curvelet子带;再采用改进完全局部二值模式(Improved Completed Local Binary Pattern,ICLBP)提取Curvelet子带的纹理特征,并生成分块统计直方图,将统计直方图级联作为一种新的声音事件特征;最后,使用支持向量机作为分类器对16种声音事件在不同噪声和不同信噪比下进行识别。实验结果表明,所提特征与其他声音特征相比,可以有效识别各种噪声环境下不同种类的声音事件。
英文摘要:
      A sound event recognition method based on texture features of cochleagram is proposed for improving sound event recognition in various environments. Firstly, the original sound sample is converted into a grayscale cochleagram by Gammatone filter bank. Then, the cochleagram is processed by Curvelet transform to obtain Curvelet sub-bands with different scales and directions. The texture features of Curvelet sub-bands are extracted by using the improved completed local binary pattern (ICLBP) to generate the block statistical histograms which are cascaded as a new sound event feature for recognition. Finally, the support vector machine is used as a classifier to identify 16 kinds of sound events under different noise environments and different signal-to-noise ratios. The experimental results show that the proposed algorithm can effectively identify different kinds of sound events in various noise environments compared with other sound features.
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