dl4mir: A Tutorial on Deep Learning for MIR

by Keunwoo Choi ([email protected])

This is a repo for my tutorial paper; A Tutorial on Deep Learning for Music Information Retrieval.

Tutorials

1. Example 1: Pitch detector with a dense layer
2. Example 2: Chord recogniser with a convnet
3. Example 3: Setup config.json
4. Example 4: download and preprocess
5. Real examples with real datasets! * Example 5-1: Time-varying classification example using Jamendo dataset * Example 5-2: Time-invariant classification example using FMA dataset

Prerequisites

$ pip install -r requirements.txt
$ git clone https://github.com/keunwoochoi/kapre.git
$ cd kapre
$ python setup.py install

to install

• Librosa, Keras, Numpy, Matplotlib, Future
• kapre

Notes

• Datasets is removed from Kapre and the codes are directly imported into here.

Datasets

Dataset management

• GTZan: (30s, 10 genres, 1,000 mp3)
• MagnaTagATune: (29s, 188 tags, 25,880 mp3) for tagging and triplet similarity
• MusicNet: (full length 330 classicals music, note-wise annotations)
• FMA: small/medium/large/full collections, up to 100+K songs from free music archieve, for genre classification. With genre hierarchy, pre-computed features, splits, etc.
• Jamendo: 61/16/24 songs for vocal activity detection

Some links

• Repo
  • DL_MIR_TUTORIAL: Another DL for MIR tutorial repo by Thomas Lidy
  • Awesome deep learning music: A long list of deep learning x music papers + etc.
• Slides
  • Deep Neural Networks in MIR: A tutorial focusing on feature learning, beat/rhythm analysis, structure analysis. Also a nice literature overview including publications by year, conference, task, network types, input representations, frame work, etc. By Meinard Muller et al.
  • DL in music informatics: ISMIR 2014 tutorial.
• Documents, books
  • Deep learning book: The first deep learning textbook. by Ian Goodfellow and Yoshua Bengio and Aaron Courville.
• Online
  • cs213n: Perhaps the best lecture on convnets. From Stanford university.
  • librosa tutorial: If you're interested in learning a bit more of MIR and its implementations.
  • MIRDL: state-of-the-art: A wikipedia on MIR and DL by Jordi Pons.
  • MIR datasets: An awesome list of MIR datasets
  • /r/musicir: A subreddit for MIR

Cite?

@article{choi2017tutorial,
  title={A Tutorial on Deep Learning for Music Information Retrieval},
  author={Choi, Keunwoo and Fazekas, Gy{\"o}rgy and Cho, Kyunghyun and Sandler, Mark},
  journal={arXiv:1709.04396},
  year={2017}
}

Or visit the paper page on Google scholar for potential updates.