Learning Features of Music from Scratch
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.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2016
- DOI:
- 10.48550/arXiv.1611.09827
- arXiv:
- arXiv:1611.09827
- Bibcode:
- 2016arXiv161109827T
- Keywords:
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- Statistics - Machine Learning;
- Computer Science - Machine Learning;
- Computer Science - Sound
- E-Print:
- 14 pages