title: 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. 
authors: John Thickstun Zaid Harchaoui Sham Kakade
hIndex: Sham Kakade 76