| 摘 要: 针对传统音乐流派分类模型性能不稳定、音乐信号特征单一导致分类准确率低的问题，提出了改进的BP神经网络(Back Propagation Neural Network)音乐流派分类模型，通过Python的Librosa库提取了音乐的均方根能量、过零率、频谱质心、频谱对比度等多种特征，并使用PCA(Principal Component Analysis)和LDA(Linear Discriminant Analysis)数据降维方法对特征数据进行可视化分析，证明了特征选取的合理性。最后对四类音乐流派进行仿真实验，并与传统的分类模型对比。实验证明，提出的模型10 折交叉验证的准确率为93.12%，优于KNN(K-Nearest Neighbor)、SVM(Support Vector Machine)等传统的分类模型。 |
| 关键词: 机器学习；特征提取；支持向量机；梅尔频率倒谱系数 |
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中图分类号: TP39
文献标识码: A
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| Music Genre Classification based on Improved Back Propagation Neural Network |
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FAN Sihan
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(Department of Science, Zhejiang Sci-Tech University, Hangzhou 310000, China)
1603923623@qq.com
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| Abstract: Classification models of traditional music genre have problems of unstable performance and low classification accuracy caused by single characteristics of music signals. In view of these problems, this paper proposes an improved BP (Back Propagation) Neural Network music genre classification model. Various features such as root mean square energy, zero crossing rate, spectral centroid, and spectral contrast of music are extracted through the Librosa library of Python. Then, visual analysis of the feature data using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) data dimensionality reduction methods has proved the rationality of feature selection. Finally, simulation experiments are conducted on four types of music genres, and compared with the traditional classification model. The experiment proves that the accuracy of the 10-fold cross-validation of the model proposed in this paper is 93.12%, which is better than traditional classification models such as KNN (K-Nearest Neighbor) and SVM (Support Vector Machine). |
| Keywords: machine learning; feature extraction; SVM; MFCC |
