用群对称性让音乐模型更懂和弦进行的生成逻辑
Music102: An $D_{12}$-equivariant transformer for chord progression accompaniment
- 基于十二平均律对称性设计等变Transformer架构
- 在POP909上比非等变模型精度更高，参数更少
- 适合研究音乐生成与计算作曲的开发者
我们提出Music102，一种基于$D_{12}$等变变换器的和弦进行伴奏增强模型。受群论与符号音乐结构启发，Music102利用音乐对称性（如移调、反射）并将其融入Transformer架构，使模型在旋律与和弦序列上保持等变性。在POP909数据集上训练与评估，尽管参数更少，其加权损失与精确度均显著优于非等变的Music101原型。该工作展示了自注意力机制与层归一化在离散音乐领域的可适配性，解决了计算音乐分析中的挑战。凭借稳定灵活的神经框架，Music102为等变音乐生成与计算作曲工具的发展奠定基础。
原文摘要 · Abstract (English)
We present Music102, an advanced model aimed at enhancing chord progression accompaniment through a $D_{12}$-equivariant transformer. Inspired by group theory and symbolic music structures, Music102 leverages musical symmetry--such as transposition and reflection operations--integrating these properties into the transformer architecture. By encoding prior music knowledge, the model maintains equivariance across both melody and chord sequences. The POP909 dataset was employed to train and evaluate Music102, revealing significant improvements over the non-equivariant Music101 prototype Music101 in both weighted loss and exact accuracy metrics, despite using fewer parameters. This work showcases the adaptability of self-attention mechanisms and layer normalization to the discrete musical domain, addressing challenges in computational music analysis. With its stable and flexible neural framework, Music102 sets the stage for further exploration in equivariant music generation and computational composition tools, bridging mathematical theory with practical music performance.
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