@report {1616681,
	title = {Learning Features of Music from Scratch},
	year = {2017},
	institution = {ArXiv Report},
	address = {ICLR},
	abstract = {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.},
	url = {https://arxiv.org/abs/1611.09827},
	author = {John Thickstun and Zaid Harchaoui and Sham Kakade}
}
