Constructing Composite Features for Interpretable Music-Tagging
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 abstractio...
| Authors | Chenhao Xue, Weitao Hu, Joyraj Chakraborty et al. |
|---|---|
| Primary category | cs.SD |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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