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Learning Features of Music From Scratch
John Thickstun, Zaid Harchaoui, Sham Kakade
Nov 04, 2016 (modified: Mar 03, 2017)ICLR 2017 conference submissionreaders: everyone
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.
TL;DR:We introduce a new large-scale music dataset, define a multi-label classification task, and benchmark machine learning architectures on this task.
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