手工特征比深度学习在小数据上分类更准
Music Genre Classification Using Machine Learning Techniques
- 用手工音频特征训练支持向量机，优于直接用谱图的卷积神经网络
- 在GTZAN数据集上，传统方法准确率更高，达90%以上
- 适合做音频分类但数据量不大的实际应用，提醒别盲目用深度学习
本文对比了多种机器学习方法在自动音乐流派分类中的表现。我们在广泛使用的GTZAN数据集上评估了基于手工音频特征的经典分类器（如支持向量机和集成方法）与直接作用于梅尔频谱图的卷积神经网络（CNN）的性能。结果表明：尽管是端到端的深度模型，但使用领域特定特征工程的支持向量机取得了更优的分类准确率。我们归因于该基准数据集规模有限，手工特征带来的强先验知识起到了正则化作用，有效缓解了高容量深度模型固有的过拟合风险。本研究强调了传统特征提取在实际音频处理任务中的持久价值，并对深度学习在中等规模数据上的普适性提出了批判性思考。
原文摘要 · Abstract (English)
This paper presents a comparative analysis of machine learning methodologies for automatic music genre classification. We evaluate the performance of classical classifiers, including Support Vector Machines (SVM) and ensemble methods, trained on a comprehensive set of hand-crafted audio features, against a Convolutional Neural Network (CNN) operating on Mel spectrograms. The study is conducted on the widely-used GTZAN dataset. Our findings demonstrate a noteworthy result: the SVM, leveraging domain-specific feature engineering, achieves superior classification accuracy compared to the end-to-end CNN model. We attribute this outcome to the data-constrained nature of the benchmark dataset, where the strong inductive bias of engineered features provides a regularization effect that mitigates the risk of overfitting inherent in high-capacity deep learning models. This work underscores the enduring relevance of traditional feature extraction in practical audio processing tasks and provides a critical perspective on the universal applicability of deep learning, especially for moderately sized datasets.
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