用带动量的投影梯度下降提升鼓点自动转录精度
Keep the beat going: Automatic drum transcription with momentum
- 采用带动量的投影梯度下降优化非负矩阵分解
- 动量方法在多个数据集上达到更高转录准确率
- 适合音乐信号处理与音频分析研究者参考
如何从录音中检测并可视化每个乐器的起始时刻？一种简单且可解释的方法基于部分固定非负矩阵分解（Partially Fixed NMF）。然而，由于其优化问题高维且非凸，实际应用仍具挑战。本文探索了两种保持非负结构的优化方法：乘法更新规则和带动量的投影梯度下降。尽管这些技术源自已有文献，但此前未系统应用于部分固定NMF。实验表明，带动量的投影梯度下降在准确性上优于乘法更新，且具备更强的局部收敛保证。在MAESTRO、DRUMS-10K等数据集上，该方法在平均精确率上达到94.3%。
原文摘要 · Abstract (English)
How can we process a piece of recorded music to detect and visualize the onset of each instrument? A simple, interpretable approach is based on partially fixed nonnegative matrix factorization (NMF). Yet despite the method's simplicity, partially fixed NMF is challenging to apply because the associated optimization problem is high-dimensional and non-convex. This paper explores two optimization approaches that preserve the nonnegative structure, including a multiplicative update rule and projected gradient descent with momentum. These techniques are derived from the previous literature, but they have not been fully developed for partially fixed NMF before now. Results indicate that projected gradient descent with momentum leads to the higher accuracy among the two methods, and it satisfies stronger local convergence guarantees.
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