Abstract
A simple, interpretable way to perform automatic drum transcription is by factoring the magnitude spectrogram of a recorded musical piece using a partially fixed nonnegative matrix factorization. There are two natural ways to optimize the nonnegative matrix factorization, including a multiplicative update rule and projected gradient descent with momentum. The methods differ in their empirical accuracies and theoretical convergence guarantees. This paper summarizes the methods and their time complexities, and it applies the methods to the ENST-Drums data set and an original recording from the author's band, evaluating the empirical accuracy with respect to ground-truth drum annotations. The results indicate that projected gradient descent with momentum leads to higher accuracy for a fixed runtime, and it satisfies stronger convergence guarantees.
Abstract (translated)
一种简单且易于解释的自动鼓谱转录方法是通过使用部分固定的非负矩阵分解来因子化录制音乐作品的幅度频谱图。优化非负矩阵分解有两种自然的方法：乘法更新规则和带有动量的投影梯度下降。这些方法在经验准确性和理论收敛保证方面有所不同。 本文总结了这两种方法及其时间复杂性，并将它们应用于ENST-Drums数据集以及作者乐队的一段原始录音，以根据地面实况鼓标记评估其经验准确性。实验结果表明，在固定运行时间内，带有动量的投影梯度下降法能够达到更高的准确率，并且在收敛保证方面也更强。
URL
https://arxiv.org/abs/2507.12596
