A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network
A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network
复制标题
使用卷积神经网络进行音乐节奏估计的单步方法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Meinard Müller
中科院分区：
文献类型：
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作者：
Hendrik Schreiber;Meinard Müller
提出了一种完全基于卷积神经网络的单步音乐节奏估计系统。与现有系统不同的是，我们的系统通常是fi首先识别开始或节拍，然后得出节拍，而我们的系统直接从传统的MEL谱图在单个步骤中估计节拍。这是通过使用受传统方法启发的网络体系结构将节奏估计帧化为多类Classifi阳离子问题来实现的。该系统的cnn已经接受了三个数据集的联合训练，涵盖了大量不同的流派和节奏，使用了特定问题的fic数据增强技术。三个基本事实中有两个是新奇的，将用于研究目的。作为输入，该系统只需要11个。9 S的音频，因此适合于本地以及全球的节奏估计。当用作全局估计器时，它的性能与其他最先进的算法一样，甚至更好。特别是在没有节奏八度混淆的情况下，对节奏的准确估计得到了显著的改善。fi。作为局部估计器，它可以用来识别和可视化音乐表演中的节奏漂移。
We present a single-step musical tempo estimation system based solely on a convolutional neural network (CNN). Contrary to existing systems, which typically first identify onsets or beats and then derive a tempo, our sys-tem estimates the tempo directly from a conventional mel-spectrogram in a single step. This is achieved by framing tempo estimation as a multi-class classification problem using a network architecture that is inspired by conventional approaches. The system’s CNN has been trained with the union of three datasets covering a large variety of genres and tempi using problem-specific data augmentation techniques. Two of the three ground-truths are novel and will be released for research purposes. As input the system requires only 11 . 9 s of audio and is therefore suitable for local as well as global tempo estimation. When used as a global estimator, it performs as well as or better than other state-of-the-art algorithms. Especially the exact estimation of tempo without tempo octave confusion is significantly improved. As local estimator it can be used to identify and visualize tempo drift in musical performances.
深度（呃）学习。
DOI：
10.1523/jneurosci.0153-18.2018
发表时间：
2018
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
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作者：
Srinivasan,Shyam;Greenspan,RalphJ;Stevens,CharlesF;Grover,Dhruv
通讯作者：
Grover,Dhruv