Abstract
Combining multiple audio features can improve the performance of music tagging, but common deep learning-based feature fusion methods often lack interpretability. To address this problem, we propose a Genetic Programming (GP) pipeline that automatically evolves composite features by mathematically combining base music features, thereby capturing synergistic interactions while preserving interpretability. This approach provides representational benefits similar to deep feature fusion without sacrificing interpretability. Experiments on the MTG-Jamendo and GTZAN datasets demonstrate consistent improvements compared to state-of-the-art systems across base feature sets at different abstraction levels. It should be noted that most of the performance gains are noticed within the first few hundred GP evaluations, indicating that effective feature combinations can be identified under modest search budgets. The top evolved expressions include linear, nonlinear, and conditional forms, with various low-complexity solutions at top performance aligned with parsimony pressure to prefer simpler expressions. Analyzing these composite features further reveals which interactions and transformations tend to be beneficial for tagging, offering insights that remain opaque in black-box deep models.
Abstract (translated)
结合多种音频特征可以提升音乐标签任务的性能，但常见的基于深度学习的特征融合方法往往缺乏可解释性。针对这一问题，我们提出一种遗传编程（GP）流程，该流程通过数学方式组合基础音乐特征，自动演化复合特征，从而在保持可解释性的同时捕捉协同交互效应。该方法在提供与深度特征融合相似的表示优势的同时，并未牺牲可解释性。在MTG-Jamendo和GTZAN数据集上的实验表明，与最先进系统相比，该方法在不同抽象层级的基础特征集上均取得持续改进。值得注意的是，大部分性能提升出现在前几百次GP评估中，表明在适度的搜索预算下即可识别出有效的特征组合。排名靠前的演化表达式包含线性、非线性及条件形式，其中多种低复杂度方案因受精简压力影响而优先采用更简洁的表达式，同时保持顶尖性能。进一步分析这些复合特征可揭示哪些交互与变换对标签任务有益，而这些洞见在黑盒深度模型中仍不透明。
URL
https://arxiv.org/abs/2603.28644
