压缩音频动态范围可提升音乐流派分类准确率3.1%。
Dynamic Range Compression and Its Effect on Music Genre Classification
- 对测试集歌曲施加不同参数的动态范围压缩
- 平均分类准确率提升3.1%，最优参数随训练数据变化
- 适合音乐推荐系统与音频处理研究者参考
本文研究动态范围压缩（DRC）对音乐流派分类准确率的影响。通过对200首歌曲的测试集应用多种压缩设置，评估压缩是否能增强分类器识别不同音乐流派的能力。使用支持向量机（SVM）在原始未压缩数据上训练分类器，考察阈值、比率、膝宽、攻击时间、释放时间和增益补偿等参数的影响。结果表明，对测试集应用压缩后，分类准确率平均提升3.1%。最优压缩参数在不同实验中存在差异，说明压缩效果依赖于模型训练数据。研究提供了1000次训练-测试划分下的最佳压缩参数表。结论表明，动态范围压缩可作为提升音乐流派分类性能的有效预处理手段，为更精准可靠的音乐推荐系统提供支持。
原文摘要 · Abstract (English)
This paper investigates the impact of dynamic range compression (DRC) on music genre classification accuracy. By applying various compression settings to the test set of 200 songs, we aim to determine if compression can enhance the classifier's ability to discern distinct musical genres. A support vector machine (SVM) classifier was trained on the original, uncompressed dataset. The study explored the influence of threshold, ratio, knee width, attack time, release time, and makeup gain on classification performance. Our findings indicate that applying compression to the test set can indeed improve music genre classification accuracy on average by 3.1%. The optimal compression settings varied across experiments, suggesting that the effectiveness of compression depends on the training data of the model. A table of the top compression settings over 1000 train and test splits is provided. In conclusion, this research demonstrates that dynamic range compression can serve as a valuable preprocessing technique for enhancing music genre classification. The insights gained from this study can inform the development of more accurate and robust music recommendation systems.
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