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Expand Up @@ -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.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.

</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 +224,8 @@ Dataset lists:
* <https://cloudlab.atlassian.net/wiki/display/datasets/FMA:+A+Dataset+For+Music+Analysis>
* <https://www.datasetlist.com>

</details>

## History

**2017-05-09 pre-publication release**
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