用Mamba架构实现高效自动和弦识别，参数少、算力低
A Mamba-Based Model for Automatic Chord Recognition
- 采用双向Mamba结构建模音乐时序依赖
- 性能媲美顶尖模型，参数量更少
- 适合资源受限场景的实时和弦识别
本文提出一种基于Mamba的新型高效解决方案——BMACE（双向Mamba网络，用于自动和弦估计），通过在双向Mamba层中引入选择性结构状态空间模型，有效捕捉音乐信号中的时序依赖关系。该模型在保持与当前最优模型相当预测性能的同时，显著降低了参数量与计算开销，展现出更强的实用性与部署优势。
原文摘要 · Abstract (English)
In this work, we propose a new efficient solution, which is a Mamba-based model named BMACE (Bidirectional Mamba-based network, for Automatic Chord Estimation), which utilizes selective structured state-space models in a bidirectional Mamba layer to effectively model temporal dependencies. Our model achieves high prediction performance comparable to state-of-the-art models, with the advantage of requiring fewer parameters and lower computational resources
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