In recent years, various well-designed algorithms have empowered music platforms to provide content based on one's preferences. Music genres are defined through various aspects, including acoustic features and cultural considerations. Music genre classification works well with content-based filtering, which recommends content based on music similarity to users. Given a considerable dataset, one premise is automatic annotation using machine learning or deep learning methods that can effectively classify audio files. The effectiveness of systems largely depends on feature and model selection, as different architectures and features can facilitate each other and yield different results. In this study, we conduct a comparative study investigating the performances of three models: a proposed convolutional neural network (CNN), the VGG16 with fully connected layers (FC), and an eXtreme Gradient Boosting (XGBoost) approach on different features: 30-second Mel spectrogram and 3-second Mel-frequency cepstral coefficients (MFCCs). The results show that the MFCC XGBoost model outperformed the others. Furthermore, applying data segmentation in the data preprocessing phase can significantly enhance the performance of the CNNs.

中文翻译：

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音乐流派分类：使用梅尔频率倒谱系数和梅尔谱图对 CNN 和 XGBoost 方法进行比较分析

近年来，各种精心设计的算法使音乐平台能够根据个人喜好提供内容。音乐流派是通过各个方面来定义的，包括声学特征和文化考虑。音乐流派分类与基于内容的过滤配合得很好，后者根据音乐相似度向用户推荐内容。给定大量数据集，一个前提是使用机器学习或深度学习方法进行自动注释，可以有效地对音频文件进行分类。系统的有效性很大程度上取决于特征和模型的选择，因为不同的架构和特征可以相互促进并产生不同的结果。在本研究中，我们对三种模型的性能进行了比较研究：提出的卷积神经网络 (CNN)、具有全连接层 (FC) 的 VGG16 以及针对不同特征的极限梯度提升 (XGBoost) 方法：30-第二个梅尔频谱图和 3 秒梅尔频率倒谱系数 (MFCC)。结果表明，MFCC XGBoost 模型优于其他模型。此外，在数据预处理阶段应用数据分割可以显着提高 CNN 的性能。