用音频特征+神经网络实现5类音乐流派精准分类
Audio Processing using Pattern Recognition for Music Genre Classification
- 提取频谱滚降、质心和梅尔频率倒谱系数作为特征
- 神经网络模型达到92.44%验证准确率，表现最优
- 适合对音乐推荐系统优化感兴趣的开发者
本研究利用机器学习技术对音乐流派进行分类，基于包含每类100个音频文件的GTZAN数据集，聚焦蓝调、古典、爵士、嘻哈和乡村五类。采用逻辑回归、K近邻、随机森林及基于Keras实现的人工神经网络等算法，其中神经网络模型表现最佳，验证准确率达92.44%。通过分析频谱滚降、频谱质心和梅尔频率倒谱系数（MFCCs）等关键音频特征，提升了分类精度。未来将扩展至全部十类，并探索长短期记忆（LSTM）网络与集成方法，开发支持实时流派识别与播放列表生成的Web应用，旨在提升音乐推荐系统与内容管理效果。
原文摘要 · Abstract (English)
This project explores the application of machine learning techniques for music genre classification using the GTZAN dataset, which contains 100 audio files per genre. Motivated by the growing demand for personalized music recommendations, we focused on classifying five genres-Blues, Classical, Jazz, Hip Hop, and Country-using a variety of algorithms including Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, and Artificial Neural Networks (ANN) implemented via Keras. The ANN model demonstrated the best performance, achieving a validation accuracy of 92.44%. We also analyzed key audio features such as spectral roll-off, spectral centroid, and MFCCs, which helped enhance the model's accuracy. Future work will expand the model to cover all ten genres, investigate advanced methods like Long Short-Term Memory (LSTM) networks and ensemble approaches, and develop a web application for real-time genre classification and playlist generation. This research aims to contribute to improving music recommendation systems and content curation.
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