基于HHT算法的扬声器异常音检测技术  

Loudspeaker detection technology based on HHT algorithm

作　　者：李云红[1] 李小英[1] 周静雷[1] 潘杨[1] 

机构地区：[1]西安工程大学电子信息学院,西安710048

出　　处：《计算机工程与应用》2015年第11期212-217,共6页Computer Engineering and Applications

基　　金：西安市科技局技术转移促进工程项目(No.CXY1431(4));国家级大学生创新创业训练计划项目(No.201310709004;No.201410709024);西安工程大学大学生创新创业训练计划项目(No.201403018);陕西省教育厅科研计划资助项目(No.14JK1319);陕西省科技厅自然科学基础研究计划资助项目(No.2013JC2-15)

摘　　要：为了有效地检测扬声器异常音,提出一种基于希尔伯特-黄变换的异常音识别及去噪方法。分析了小波变换的不足,再进行HHT分析。建立常见的几类扬声器异常音数学模型,并经EMD分解得到包含异常振动信息的IMF分量,利用IMF分量特点进行去噪处理。再求出各模拟异常音信号的Hilbert谱,采用图像二值化技术处理Hilbert谱,和小波变换时频谱比较后证明了HHT方法具有更多的优势。而且,根据不同类型异常音在时频域的特征,即可判断出扬声器异常音类型。matlab仿真实验结果表明,当归一化阈值取为0.065时,会得到清晰的图像,能更精确地检测出异常音类型。In order to detect the loudspeaker defects effectively, a new method of recognition and denoising based on HHT algorithm for loudspeaker defects is presented. The shortcomings of the wavelet transform are analyzed, the HHT analysis again. Several loudspeaker defect models are established to be processed by the use of the EMD method, in which the characteristics of the IMF component including abnormal vibration information are obtained for denoising. The Hilbert spectrum of loudspeaker defect signal is obtained to be processed by the use of the image binarization technology.It proves that the HHT method has more advantages in comparison with wavelet transform. What＇s more, according to the characteristics of different loudspeaker defects in the time-frequency domain, the types of the loudspeaker defects can be well detected. Matlab simulation results show that, a clear image will appear when the normalized threshold is 0.065,with which the types of loudspeaker defects can be detected more accurately.

关 键 词：异常音检测 希尔伯特-黄变换(HHT) 小波变换(WT) 

分 类 号：TB523[理学—物理] TB54[理学—声学]