This paper introduces a new large-scale music dataset, MusicNet, to serve as
a source of supervision and evaluation of machine learning methods for music
research. MusicNet consists of hundreds of freely-licensed classical music
recordings by 10 composers, written for 11 instruments, together with
instrument/note annotations resulting in over 1 million temporal labels on 34
hours of chamber music performances under various studio and microphone
conditions.
The paper defines a multi-label classification task to predict notes in
musical recordings, along with an evaluation protocol, and benchmarks several
machine learning architectures for this task: i) learning from spectrogram
features; ii) end-to-end learning with a neural net; iii) end-to-end learning
with a convolutional neural net. These experiments show that end-to-end models
trained for note prediction learn frequency selective filters as a low-level
representation of audio.

本文介绍了一个新的大规模音乐数据集 MusicNet，旨在为音乐研究的机器学习方法提供监督和评估，该数据集由 10 位作曲家创作的 11 种乐器的数百个自由许可的古典音乐录音以及关于它们的时间标签组成，其中包含的多标签分类任务以及多项基于机器学习结构的性能评估表明了末端到末端的神经网络在音符预测方面的学习效果最佳。