基于自适应最优核时频分布的鸟类识别

摘要

A bird identification method for the transient characteristics of birdsong signal based on adap‐tive optimal kernel(AOK) time‐frequency distribution identification is proposed .The collected birdsong signal is preprocessed and the spectrum is obtained through the AOK time‐frequency analysis method , Different energy distribution of birds sound signal at different time and different frequency are also ana‐lyzed .Then diagram spectrum is turned into gray image ,the gray level co‐occurrence matrix is calculat‐ed ,image features is extracted as the eigenvalues of birds identification based on gray co‐occurrence ma‐trix parameters at different angles .Finally ,the image texture of the known species is selected to gener‐ate training template and the image texture characteristic parameters of the species for identifying is used to generate the test template ,Template matching is achieved using dynamic time warping (DTW) algo‐rithm .The matching value are compared to find the minimum matching value corresponding templates , therefore the recognition of birds are realized .Finally ,40 kinds of common birds experiments demon‐strate that the overall recognition rate reaches 96% .%针对鸟鸣声信号的非稳态特性，提出了一种基于自适应最优核时频分布（Adaptive optimal ker‐nel ，AOK）的鸟类识别方法。首先对采集的鸟鸣声信号进行预处理，通过AOK时频分析方法得到时频谱图，分析不同鸟类声音信号在不同时间和不同频率下的能量分布。然后，将时频谱图转化成灰度图像，求取灰度共生矩阵，提取基于灰度共生矩阵不同角度的图像特征参数作为鸟类识别的特征值。最后选取已知鸟种的图像纹理特征训练生成训练模板，将待识别的鸟种的图像纹理特征参数生成测试模板，利用动态规整（Dynamic time warping ，DTW）算法进行模板的匹配，将匹配值进行大小比较，找到最小匹配值对应的模板，从而实现鸟类的识别。通过对40种常见鸟类的实验表明，总体识别率达到96％。