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@JustinKavalan
JustinKavalan committed Jun 19, 2020
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pip install numpy==1.12.1 # workaround resampy's bogus setup.py
pip install -r requirements.txt
```
Note: depending on your usage, you may need to install [ffmpeg](https://ffmpeg.org/download.html) or [graphviz](https://www.graphviz.org).\
Note: you may need to install [ffmpeg](https://ffmpeg.org/download.html) or [graphviz](https://www.graphviz.org) depending on your usage.\
Note: install [CUDA](https://en.wikipedia.org/wiki/CUDA) to train neural networks on GPUs (see [Tensorflow's instructions](https://www.tensorflow.org/install/)).
1. Download some data, verify its integrity, and uncompress the archives.
Expand Down Expand Up @@ -146,8 +146,8 @@ The following notebooks, scripts, and modules have been developed for the datase
cd ..
```
Note: try [7zip](https://www.7-zip.org) if you get any error while decompressing the archives (especially with the Windows and macOS system unzippers).
That is probably an [unsupported compression issue](https://github.com/mdeff/fma/issues/5).
Note: try [7zip](https://www.7-zip.org) if decompression errors.
It might be an [unsupported compression issue](https://github.com/mdeff/fma/issues/5).
1. Fill a `.env` configuration file (at repository's root) with the following content.
```
Expand All @@ -161,24 +161,13 @@ The following notebooks, scripts, and modules have been developed for the datase
make usage.ipynb
```

## Coverage and resources
## Impact, coverage, and resources

* [Using CNNs and RNNs for Music Genre Recognition](https://towardsdatascience.com/using-cnns-and-rnns-for-music-genre-recognition-2435fb2ed6af), Towards Data Science, 2018-12-13.
* [Over 1.5 TB’s of Labeled Audio Datasets](https://towardsdatascience.com/a-data-lakes-worth-of-audio-datasets-b45b88cd4ad), Towards Data Science, 2018-11-13.
* [Genre recognition challenge](https://www.crowdai.org/challenges/www-2018-challenge-learning-to-recognize-musical-genre) at the [web conference](https://www2018.thewebconf.org/program/challenges-track/), Lyon, 2018-04.
* [Discovering Descriptive Music Genres Using K-Means Clustering](https://medium.com/latinxinai/discovering-descriptive-music-genres-using-k-means-clustering-d19bdea5e443), Medium, 2018-04-09.
* [25 Open Datasets for Deep Learning Every Data Scientist Must Work With](https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/), Analytics Vidhya, 2018-03-29.
* [music2vec: Generating Vector Embeddings for Genre-Classification Task](https://medium.com/@rajatheb/music2vec-generating-vector-embedding-for-genre-classification-task-411187a20820), Medium, 2017-11-28.
* [Slides](https://doi.org/10.5281/zenodo.1066119) presented at the [Data Jam days](http://datajamdays.org), Lausanne, 2017-11-24.
* [Poster](https://doi.org/10.5281/zenodo.1035847) presented at [ISMIR 2017](https://ismir2017.smcnus.org), China, 2017-10-24.
* [Slides](https://doi.org/10.5281/zenodo.999353) for the [Open Science in Practice](https://osip2017.epfl.ch) summer school at EPFL, 2017-09-29.
* [A Music Information Retrieval Dataset, Made With FMA](https://web.archive.org/web/20190907182116/http://freemusicarchive.org/member/cheyenne_h/blog/A_Music_Information_Retrieval_Dataset_Made_With_FMA), freemusicarchive.org, 2017-05-22.
* [Pre-publication release announced](https://twitter.com/m_deff/status/861985446116589569), twitter.com, 2017-05-09.
* [FMA: A Dataset For Music Analysis](https://tensorflow.blog/2017/03/14/fma-a-dataset-for-music-analysis), tensorflow.blog, 2017-03-14.
* [Beta release discussed](https://twitter.com/YadFaeq/status/829406463286063104), twitter.com, 2017-02-08.
* [FMA Data Set for Researchers Released](https://web.archive.org/web/20190826112752/http://freemusicarchive.org/member/cheyenne_h/blog/FMA_Dataset_for_Researchers), freemusicarchive.org, 2016-12-15.
<details><summary>100+ research papers</summary>

Full list on [Google Scholar](https://scholar.google.com/scholar?cites=13646959466952873682,13785796238335741238,7544459641098681164,5736399534855095976).
Some picks below.

Research papers (see also [citations on Google Scholar](https://scholar.google.com/scholar?cites=13646959466952873682,13785796238335741238)):
* [Zero-shot Learning for Audio-based Music Classification and Tagging](https://arxiv.org/abs/1907.02670)
* [One deep music representation to rule them all? A comparative analysis of different representation learning strategies](https://doi.org/10.1007/s00521-019-04076-1)
* [Deep Learning for Audio-Based Music Classification and Tagging: Teaching Computers to Distinguish Rock from Bach](https://sci-hub.tw/10.1109/MSP.2018.2874383)
Expand All @@ -190,7 +179,41 @@ Research papers (see also [citations on Google Scholar](https://scholar.google.c
* [Learning to Recognize Musical Genre from Audio: Challenge Overview](https://arxiv.org/abs/1803.05337)
* [Representation Learning of Music Using Artist Labels](https://arxiv.org/abs/1710.06648)

Dataset lists:
</details>

<details><summary>2 derived works</summary>

* [OpenMIC-2018: An Open Data-set for Multiple Instrument Recognition](https://github.com/cosmir/openmic-2018)
* [ConvNet features](https://github.com/keunwoochoi/FMA_convnet_features) from [Transfer learning for music classification and regression tasks](https://arxiv.org/abs/1703.09179)

</details>

<details><summary>~10 posts</summary>

* [Using CNNs and RNNs for Music Genre Recognition](https://towardsdatascience.com/using-cnns-and-rnns-for-music-genre-recognition-2435fb2ed6af), Towards Data Science, 2018-12-13.
* [Over 1.5 TB’s of Labeled Audio Datasets](https://towardsdatascience.com/a-data-lakes-worth-of-audio-datasets-b45b88cd4ad), Towards Data Science, 2018-11-13.
* [Discovering Descriptive Music Genres Using K-Means Clustering](https://medium.com/latinxinai/discovering-descriptive-music-genres-using-k-means-clustering-d19bdea5e443), Medium, 2018-04-09.
* [25 Open Datasets for Deep Learning Every Data Scientist Must Work With](https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/), Analytics Vidhya, 2018-03-29.
* [music2vec: Generating Vector Embeddings for Genre-Classification Task](https://medium.com/@rajatheb/music2vec-generating-vector-embedding-for-genre-classification-task-411187a20820), Medium, 2017-11-28.
* [A Music Information Retrieval Dataset, Made With FMA](https://web.archive.org/web/20190907182116/http://freemusicarchive.org/member/cheyenne_h/blog/A_Music_Information_Retrieval_Dataset_Made_With_FMA), freemusicarchive.org, 2017-05-22.
* [Pre-publication release announced](https://twitter.com/m_deff/status/861985446116589569), twitter.com, 2017-05-09.
* [FMA: A Dataset For Music Analysis](https://tensorflow.blog/2017/03/14/fma-a-dataset-for-music-analysis), tensorflow.blog, 2017-03-14.
* [Beta release discussed](https://twitter.com/YadFaeq/status/829406463286063104), twitter.com, 2017-02-08.
* [FMA Data Set for Researchers Released](https://web.archive.org/web/20190826112752/http://freemusicarchive.org/member/cheyenne_h/blog/FMA_Dataset_for_Researchers), freemusicarchive.org, 2016-12-15.

</details>

<details><summary>4 events</summary>

* [Genre recognition challenge](https://www.crowdai.org/challenges/www-2018-challenge-learning-to-recognize-musical-genre) at the [Web Conference](https://www2018.thewebconf.org/program/challenges-track/), Lyon, 2018-04.
* [Slides](https://doi.org/10.5281/zenodo.1066119) presented at the [Data Jam days](http://datajamdays.org), Lausanne, 2017-11-24.
* [Poster](https://doi.org/10.5281/zenodo.1035847) presented at [ISMIR 2017](https://ismir2017.ismir.net), Suzhou, 2017-10-24.
* [Slides](https://doi.org/10.5281/zenodo.999353) for the [Open Science in Practice](https://osip2017.epfl.ch) summer school at EPFL, 2017-09-29.

</details>

<details><summary>~10 dataset lists</summary>

* <https://github.com/caesar0301/awesome-public-datasets>
* <https://archive.ics.uci.edu/ml/datasets/FMA:+A+Dataset+For+Music+Analysis>
* <http://deeplearning.net/datasets>
Expand All @@ -201,6 +224,13 @@ Dataset lists:
* <https://cloudlab.atlassian.net/wiki/display/datasets/FMA:+A+Dataset+For+Music+Analysis>
* <https://www.datasetlist.com>

</details>

## Contributing

Contribute by opening an [issue](https://github.com/mdeff/fma/issues) or a [pull request](https://github.com/mdeff/fma/pulls).
Let this repository be a hub around the dataset!

## History

**2017-05-09 pre-publication release**
Expand All @@ -219,31 +249,44 @@ Dataset lists:
* `fma_small.zip` sha1: `e731a5d56a5625f7b7f770923ee32922374e2cbf`
* `fma_medium.zip` sha1: `fe23d6f2a400821ed1271ded6bcd530b7a8ea551`

## Contributing
## Acknowledgments and Licenses

We are grateful to the [Swiss Data Science Center] ([EPFL] and [ETHZ]) for hosting the dataset.

Please cite our work if you use our code or data.

```
@inproceedings{fma_dataset,
title = {{FMA}: A Dataset for Music Analysis},
author = {Defferrard, Micha\"el and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier},
booktitle = {18th International Society for Music Information Retrieval Conference (ISMIR)},
year = {2017},
archiveprefix = {arXiv},
eprint = {1612.01840},
url = {https://arxiv.org/abs/1612.01840},
}
```

```
@inproceedings{fma_challenge,
title = {Learning to Recognize Musical Genre from Audio},
subtitle = {Challenge Overview},
author = {Defferrard, Micha\"el and Mohanty, Sharada P. and Carroll, Sean F. and Salath\'e, Marcel},
booktitle = {The 2018 Web Conference Companion},
year = {2018},
publisher = {ACM Press},
isbn = {9781450356404},
doi = {10.1145/3184558.3192310},
archiveprefix = {arXiv},
eprint = {1803.05337},
url = {https://arxiv.org/abs/1803.05337},
}
```

Please open an issue or a pull request if you want to contribute.
Let's try to keep this repository the central place around the dataset!
Links to resources related to the dataset are welcome.
## License & co
* Please cite our [paper] if you use our code or data.
```
@inproceedings{fma_dataset,
title = {{FMA}: A Dataset for Music Analysis},
author = {Defferrard, Micha\"el and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier},
booktitle = {18th International Society for Music Information Retrieval Conference (ISMIR)},
year = {2017},
archiveprefix = {arXiv},
eprint = {1612.01840},
url = {https://arxiv.org/abs/1612.01840},
}
```
* The code in this repository is released under the terms of the [MIT license](LICENSE.txt).
* The metadata is released under the terms of the [Creative Commons Attribution 4.0 International License (CC BY 4.0)][ccby40].
* We do not hold the copyright on the audio and distribute it under the terms of the license chosen by the artist.
* The dataset is meant for research purposes.
* We are grateful to the [Swiss Data Science Center] ([EPFL] and [ETHZ]) for hosting the dataset.

[ccby40]: https://creativecommons.org/licenses/by/4.0
[Swiss Data Science Center]: https://datascience.ch/collaboration-and-partnerships
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