| 孟子轩,程巍,张天予,吕君,滕鹏晓.基于非负矩阵分解的次声信号分类方法*[J].,2023,42(3):627-636 |
| 基于非负矩阵分解的次声信号分类方法* |
| Study on classification of infrasound signals based on nonnegative matrix factorization |
| 投稿时间：2022-02-28 修订日期：2023-04-26 |
| 中文摘要: |
| 在中国科学院声学所大气次声波观察网实地采集的，爆炸、地震、闪电、再入四类次声事件105组阵列数据集的基础上，提出应用非负矩阵分解的特征提取方法，对次声信号的计算机自动分类方法进行了研究。针对特征设计过程复杂的问题，本方法使用非负矩阵分解自动挖掘目标信号的隐含结构作为特征。将此特征作为支持向量机和卷积神经网络输入进行分类，以提高特征设计的效率与分类的识别准确率。研究结果指出，在测试集上的平均识别准确率达到了83.13%， 相对于传统方法，简化了特征设计过程，并取得更好的分类结果。 |
| 英文摘要: |
| On the basis of 105 sets of array data from explosion, earthquake, lightning and rocket reentry infrasonic events, which collected by the infrasonic observation website of the Institute of Acoustics, Chinese Academy of Sciences, the computer automatic classification method of non-negative matrix decomposition is studied. For the complex problem of the feature design process, this method uses non-negative matrix decomposition to automatically mine the implicit structure of the target signal as features. Using this feature for the classification experiment as the input of SVM and convolutional neural network improves the efficiency of feature design and the identification accuracy of classification. The results point out that the average recognition accuracy on the test set reaches 83.13%, which simplifies the feature design process and achieves better classification results relative to the traditional methods. |
| DOI：10.11684/j.issn.1000-310X.2023.03.022 |
| 中文关键词: 次声信号 特征提取 非负矩阵分解 信号分类 |
| 英文关键词: infrasound signal feature extraction non-negative matrix factorization signal classification |
| 基金项目:国家自然科学基金项目（面上项目，重点项目，重大项目） |
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