标题： 保持节奏继续：具有动量的自动鼓乐转录 显示英文标题

标题： Keep the beat going: Automatic drum transcription with momentum

摘要： 一种简单且可解释的方法是通过使用部分固定的非负矩阵分解来对录制的音乐作品的幅度谱图进行分解，从而实现自动鼓声转录。 优化非负矩阵分解有两种自然方式，包括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与理论收敛保证方面有所不同。 本文总结了这些方法及其时间复杂度，并将这些方法应用于ENST-Drums数据集以及作者乐队的原始录音，根据真实鼓声标注评估其经验准确性。 结果表明，带有动量的投影梯度下降在固定运行时间内能够获得更高的准确性，并且满足更强的收敛保证。 显示英文摘要

摘要： 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.