#21 #7 add DadaGP MUSDB18 and ask for contributions

ybayle · Dec 15, 2023 · b252aaf · b252aaf
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diff --git a/.gitignore b/.gitignore
@@ -1,5 +1,2 @@
-paste_in_ReadMe.md
-todo.txt
-encours.txt
-
 __pycache__/
+venv/
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -9,15 +9,17 @@ You can look at the issues and help to solve them or you can add some missing ar
 Here are the steps to follow for adding one (or multiple) article:
 1. Check that the article is not already in the [dl4m.bib](dl4m.bib) file.
 2. Fork the repo.
-3. Add the desired bib entry at the beginning of [dl4m.bib](dl4m.bib). Take care to fill all this field for each bib entry (if there is no information about a field please indicate it as `fieldName = {No}`):
+3. `python -m venv venv`
+4. `.\venv\Scripts\pip.exe install -r requirements.txt` and for unix `venv/bin/pip install -r requirements.txt` that will install numpy, matplotlib, and bibtexparser
+5. Add the desired bib entry at the beginning of [dl4m.bib](dl4m.bib). Take care to fill all this field for each bib entry (if there is no information about a field please indicate it as `fieldName = {No}`):
     - Bib entry type (inproceedings, article, techreport, unpublished,...) (N.B.: Indicate arxiv article as @unpublished and if you know of a possible submission use `note = {Submitted to "NameOfJournalOrConference"}`)
     - Bib key (in the form AuthorlastnameYear, e.g. `Snow1999`
     - title (N.B.: Make sure the title is not written in all caps and that each word does not start with a capital letter.)
     - author (N.B.: Check that all authors in the pdf files are present in the bib author field and in the same order. Indeed automatic bib tools tend to mess up the order or to forget some authors.)
     - year
     - booktitle or journal
     - dataset (e.g. `dataset = {Inhouse & [Jamendo](http://www.mathieuramona.com/wp/data/jamendo/) & [RWC](https://staff.aist.go.jp/m.goto/RWC-MDB/)},`)
-        1. provide the link to the dataset  
+        1. provide the link to the dataset
         2. if multiple dataset are used, insert a ` & ` between each dataset
     - architecture (if multiple architectures are used, insert a ` & ` between each of them, e.g. `archi = {CNN & VPNN},`)
     - link (HTML link to the pdf file)
@@ -80,12 +82,8 @@ Here are the steps to follow for adding one (or multiple) article:
         year = {},
     }
     ```
-4. Check that you have installed this python package:
-    1. numpy
-    2. matplotlib
-    3. bibtexparser
-5. Launch the python script `python dl4m.py`.
-6. Submit your pull request!
+6. Launch the python script `.\venv\Scripts\python dl4m.py` and for unix `venv/bin/python dl4m.py`.
+7. Submit your pull request!
 
 ### Missing or incorrect field for an article
 

diff --git a/README.md b/README.md
@@ -1,3 +1,5 @@
+⚠️ This repo is unmaintained. While the info are still relevant, contributions to keep it up to date are welcome! A good starting point are the articles referenced here: https://github.com/ybayle/awesome-deep-learning-music/issues/5
+
 <img align="right" src="fig/logo.png">
 
 # Deep Learning for Music (DL4M) [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)
@@ -172,6 +174,7 @@ However, these surveys do not cover music information retrieval tasks that are i
 | 2017 | [End-to-end learning for music audio tagging at scale](https://arxiv.org/pdf/1711.02520.pdf) | [GitHub](https://github.com/jordipons/music-audio-tagging-at-scale-models) |
 | 2017 | [Designing efficient architectures for modeling temporal features with convolutional neural networks](http://ieeexplore.ieee.org/document/7952601/) | [GitHub](https://github.com/jordipons/ICASSP2017) |
 | 2017 | [Timbre analysis of music audio signals with convolutional neural networks](https://github.com/ronggong/EUSIPCO2017) | [GitHub](https://github.com/jordipons/EUSIPCO2017) |
+| 2017 | [The MUSDB18 corpus for music separation](https://doi.org/10.5281/zenodo.1117372) | [Website]() |
 | 2017 | [Deep learning and intelligent audio mixing](http://www.semanticaudio.co.uk/wp-content/uploads/2017/09/WIMP2017_Martinez-RamirezReiss.pdf) | No |
 | 2017 | [Deep learning for event detection, sequence labelling and similarity estimation in music signals](http://ofai.at/~jan.schlueter/pubs/phd/phd.pdf) | No |
 | 2017 | [Music feature maps with convolutional neural networks for music genre classification](https://www.researchgate.net/profile/Thomas_Pellegrini/publication/319326354_Music_Feature_Maps_with_Convolutional_Neural_Networks_for_Music_Genre_Classification/links/59ba5ae3458515bb9c4c6724/Music-Feature-Maps-with-Convolutional-Neural-Networks-for-Music-Genre-Classification.pdf?origin=publication_detail&ev=pub_int_prw_xdl&msrp=wzXuHZAa5zAnqEmErYyZwIRr2H0q01LnNEd4Wd7A15CQfdVLwdy98pmE-AdnrDvoc3-bVENSFrHt0yhaOiE2mQrYllVS9CJZOk-c9R0j_R1rbgcZugS6RtQ_.AUjPuJSF5P_DMngf-woH7W-7jdnQlbNQziR4_h6NnCHfR_zGcEa8vOyyOz5gx5nc4azqKTPQ5ZgGGLUxkLj1qCQLEQ5ThkhGlWHLyA.s6MBZE20-EO_RjRGCOCV4wk0WSFdN56Aloiraxz9hKCbJwRM2Et27RHVUA8jj9H8qvXIB6f7zSIrQgjXGrL2yCpyQlLffuf57rzSwg.KMMXbZrHsihV8DJM53xkHAWf3VebCJESi4KU4btNv9nQsyK2KnkhSQaTILKv0DSZY3c70a61LzywCBuoHtIhVOFhW5hVZN2n5O9uKQ) | No |
@@ -197,6 +200,7 @@ However, these surveys do not cover music information retrieval tasks that are i
 | 2018 | [Music theory inspired policy gradient method for piano music transcription](https://nips2018creativity.github.io/doc/music_theory_inspired_policy_gradient.pdf) | No |
 | 2019 | [Enabling factorized piano music modeling and generation with the MAESTRO dataset](https://arxiv.org/abs/1810.12247) | [GitHub](https://github.com/magenta/magenta/tree/master/magenta/models/onsets_frames_transcription) |
 | 2019 | [Generating Long Sequences with Sparse Transformers](https://arxiv.org/pdf/1904.10509.pdf) | [GitHub](https://github.com/openai/sparse_attention) |
+| 2021 | [{DadaGP: a Dataset of Tokenized GuitarPro Songs for Sequence Models}](https://archives.ismir.net/ismir2021/paper/000076.pdf) | [GitHub](https://github.com/dada-bots/dadaGP) |
 
 [Go back to top](https://github.com/ybayle/awesome-deep-learning-music#deep-learning-for-music-dl4m-)
 
@@ -244,15 +248,15 @@ Each entry in [dl4m.bib](dl4m.bib) also displays additional information:
 
 ## Statistics and visualisations
 
-- 165 papers referenced. See the details in [dl4m.bib](dl4m.bib).
+- 167 papers referenced. See the details in [dl4m.bib](dl4m.bib).
 There are more papers from 2017 than any other years combined.
 Number of articles per year:
 ![Number of articles per year](fig/articles_per_year.png)
-- If you are applying DL to music, there are [356 other researchers](authors.md) in your field.
+- If you are applying DL to music, there are [364 other researchers](authors.md) in your field.
 - 34 tasks investigated. See the list of [tasks](tasks.md).
 Tasks pie chart:
 ![Tasks pie chart](fig/pie_chart_task.png)
-- 53 datasets used. See the list of [datasets](datasets.md).
+- 55 datasets used. See the list of [datasets](datasets.md).
 Datasets pie chart:
 ![Datasets pie chart](fig/pie_chart_dataset.png)
 - 30 architectures used. See the list of [architectures](architectures.md).
@@ -261,7 +265,7 @@ Architectures pie chart:
 - 9 frameworks used. See the list of [frameworks](frameworks.md).
 Frameworks pie chart:
 ![Frameworks pie chart](fig/pie_chart_framework.png)
-- Only 45 articles (27%) provide their source code.
+- Only 47 articles (28%) provide their source code.
 Repeatability is the key to good science, so check out the [list of useful resources on reproducibility for MIR and ML](reproducibility.md).
 
 [Go back to top](https://github.com/ybayle/awesome-deep-learning-music#deep-learning-for-music-dl4m-)
@@ -301,13 +305,13 @@ A list of useful acronyms used in deep learning and music is stored in [acronyms
 
 ## Sources
 
-The list of conferences, journals and aggregators used to gather the proposed materials is stored in [sources.md](sources.md). 
+The list of conferences, journals and aggregators used to gather the proposed materials is stored in [sources.md](sources.md).
 
 [Go back to top](https://github.com/ybayle/awesome-deep-learning-music#deep-learning-for-music-dl4m-)
 
 ## Contributors
 
-- [Yann Bayle](http://yannbayle.fr/english/index.php) ([GitHub](https://github.com/ybayle)) - Instigator and principal maintainer 
+- [Yann Bayle](http://yannbayle.fr/english/index.php) ([GitHub](https://github.com/ybayle)) - Instigator and principal maintainer
 - Vincent Lostanlen ([GitHub](https://github.com/lostanlen))
 - [Keunwoo Choi](https://keunwoochoi.wordpress.com/) ([GitHub](https://github.com/keunwoochoi))
 - [Bob L. Sturm](http://www.eecs.qmul.ac.uk/~sturm/) ([GitHub](https://github.com/boblsturm))
@@ -359,23 +363,23 @@ The list of conferences, journals and aggregators used to gather the proposed ma
 
 #### Deep learning
 
-- [DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers](https://arxiv.org/abs/1711.03543) - 
+- [DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers](https://arxiv.org/abs/1711.03543) -
 - [Model Convertors](https://github.com/ysh329/deep-learning-model-convertor) - Convertors for DL frameworks and backend
 - [Deep architecture genealogy](https://github.com/hunkim/deep_architecture_genealogy) - Genealogy of DL architectures
 - [Deep Learning as an Engineer](http://www.univie.ac.at/nuhag-php/dateien/talks/3358_schlueter.pdf) - Slides from Jan Schlüter
 - [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning) - General deep learning resources
 - [Awesome Deep Learning Resources](https://github.com/endymecy/awesome-deeplearning-resources) - Papers regarding deep learning and deep reinforcement learning
 - [Awesome RNNs](https://github.com/kjw0612/awesome-rnn) - RNNs code, theory and applications
 - [Cheatsheets AI](https://github.com/kailashahirwar/cheatsheets-ai) - Cheat Sheets for Keras, neural networks, scikit-learn,...
-- [DL PaperNotes](https://github.com/dennybritz/deeplearning-papernotes) - Summaries and notes on general deep learning research papers 
+- [DL PaperNotes](https://github.com/dennybritz/deeplearning-papernotes) - Summaries and notes on general deep learning research papers
 - General [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) lists
 - [Echo State Network](http://minds.jacobs-university.de/sites/default/files/uploads/papers/PracticalESN.pdf)
 - [DL in NLP](http://ruder.io/deep-learning-nlp-best-practices/index.html#introduction) - Best practices for using neural networks by [Sebastian Ruder](http://ruder.io/)
 - [CNN overview](http://cs231n.github.io/convolutional-networks/) - Stanford Course
 - [Dilated Recurrent Neural Networks](https://arxiv.org/pdf/1710.02224.pdf) - How to improve RNNs?
 - [Encoder-Decoder in RNNs](https://machinelearningmastery.com/how-does-attention-work-in-encoder-decoder-recurrent-neural-networks/?utm_content=buffer0d2a7&utm_medium=social&utm_source=twitter.com&utm_campaign=bufferhttps://blog.recast.ai/ml-spotlight-rnn/?utm_content=bufferf19d3&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer) - How Does Attention Work in Encoder-Decoder Recurrent Neural Networks
 - [On the use of DL](https://twitter.com/randal_olson/status/927157485240311808/photo/1) - Misc fun around DL
-- [ML from scratch](https://github.com/eriklindernoren/ML-From-Scratch) - Python implementations of ML models and algorithms from scratch from Data Mining to DL 
+- [ML from scratch](https://github.com/eriklindernoren/ML-From-Scratch) - Python implementations of ML models and algorithms from scratch from Data Mining to DL
 - [Comparison of DL frameworks](https://project.inria.fr/deeplearning/files/2016/05/DLFrameworks.pdf) - Presentation describing the different existing frameworks for DL
 - [ELU > ReLU](https://arxiv.org/pdf/1511.07289.pdf) - Article describing the differences between ELU and ReLU
 - [Reinforcement Learning: An Introduction](http://incompleteideas.net/sutton/book/bookdraft2017nov5.pdf) - Book about reinforcement learning

diff --git a/authors.md b/authors.md
@@ -7,6 +7,7 @@
 - Badeau, Roland
 - Bammer, Roswitha
 - Barbieri, Francesco
+- Barthet, Mathieu
 - Bazzica, Alessio
 - Bello, Juan Pablo
 - Ben-Tal, Oded
@@ -25,6 +26,7 @@
 - Böck, Sebastian
 - Cai, Lianhong
 - Cangea, Cătălina
+- Carr, CJ
 - Cella, Carmine-Emanuele
 - Chan, Antoni B
 - Chan, TS
@@ -153,6 +155,7 @@
 - Koutini, Khaled
 - Krawczyk-Becker, Martin
 - Kum, Sangeun
+- Kumar, Adarsh
 - Kumar, Aparna
 - Kumar, Kundan
 - Kumar, Rithesh
@@ -183,6 +186,7 @@
 - Lin, Bo-Chen
 - Lin, Chi-Po
 - Liu, Jen-Yu
+- Liutkus, Antoine
 - Liò, Pietro
 - Lostanlen, Vincent
 - Maass, Marco
@@ -241,6 +245,7 @@
 - Qian, Sheng
 - Qu, Shuhui
 - Radenen, Mathieu
+- Rafii, Zafar
 - Ramírez, Marco A. Martínez
 - Reiss, Joshua D.
 - Ren, Gang
@@ -255,6 +260,7 @@
 - Sandler, Mark Brian
 - Santos, João Felipe
 - Santoso, Andri
+- Sarmento, Pedro
 - Saurous, Rif A.
 - Schedl, Markus
 - Scherer, Klaus R
@@ -287,6 +293,7 @@
 - Stasyuk, Andriy
 - Stoller, Daniel
 - Sturm, Bob L.
+- Stöter, Fabian-Robert
 - Su, Hong
 - Takahashi, Naoya
 - Takiguchi, Tetsuya
@@ -357,3 +364,4 @@
 - Zhao, An
 - Zheng, Xiaoqing
 - Zhou, Ruohua
+- Zukowski, Zack
diff --git a/datasets.md b/datasets.md
@@ -16,8 +16,8 @@ Please refer to the list of useful acronyms used in deep learning and music: [ac
 - [Beatles](http://isophonics.net/content/reference-annotations-beatles)
 - [C4S](http://mmc.tudelft.nl/users/alessio-bazzica#C4S-dataset)
 - [CCMixter](https://members.loria.fr/ALiutkus/kam/)
-- [dadaGP](https://github.com/dada-bots/dadaGP)
 - [DSD100](http://sisec17.audiolabs-erlangen.de/#/dataset)
+- [DadaGP](https://drive.google.com/drive/folders/1USNH8olG9uy6vodslM3iXInBT725zult?usp=sharing)
 - [Echo Nest Taste Profile Subset](https://labrosa.ee.columbia.edu/millionsong/tasteprofile)
 - [Free music archive](http://freemusicarchive.org/)
 - [GTzan](http://marsyas.info/downloads/datasets.html)
@@ -39,6 +39,7 @@ Please refer to the list of useful acronyms used in deep learning and music: [ac
 - [MAPS](http://www.tsi.telecom-paristech.fr/aao/en/2010/07/08/maps-database-a-piano-database-for-multipitch-estimation-and-automatic-transcription-of-music/)
 - [MIR-1K](https://sites.google.com/site/unvoicedsoundseparation/mir-1k)
 - [MSD](https://labrosa.ee.columbia.edu/millionsong/)
+- [MUSDB18](https://sigsep.github.io/datasets/musdb.html)
 - [Magnatagatune](http://mirg.city.ac.uk/codeapps/the-magnatagatune-dataset)
 - [MedleyDB](http://medleydb.weebly.com/)
 - [MusicNet](https://homes.cs.washington.edu/~thickstn/musicnet.html)

diff --git a/dl4m.bib b/dl4m.bib
@@ -1797,6 +1797,40 @@ @inproceedings{Pons2017b
   year = {2017}
 }
 
+@inproceedings{Rafii2017,
+  activation = {},
+  address = {},
+  architecture = {No},
+  author = {Rafii, Zafar and Liutkus, Antoine and Stöter, Fabian-Robert and Mimilakis, Stylianos Ioannis and Bittner, Rachel},
+  batch = {},
+  booktitle = {No},
+  code = {},
+  computationtime = {},
+  dataaugmentation = {},
+  dataset = {[MUSDB18](https://sigsep.github.io/datasets/musdb.html)},
+  dimension = {},
+  doi = {10.5281/zenodo.1117372},
+  dropout = {},
+  epochs = {},
+  framework = {No},
+  gpu = {},
+  input = {},
+  layers = {},
+  learningrate = {},
+  link = {https://doi.org/10.5281/zenodo.1117372},
+  loss = {},
+  metric = {},
+  momentum = {},
+  month = {Dec.},
+  note = {},
+  optimizer = {},
+  pages = {},
+  reproducible = {},
+  task = {Source separation},
+  title = {The MUSDB18 corpus for music separation},
+  year = {2017}
+}
+
 @inproceedings{Ramirez2017,
   address = {Salford, UK},
   architecture = {DAE},
@@ -2222,3 +2256,35 @@ @unpublished{Child2019
   year = {2019}
 }
 
+@inproceedings{Sarmento2021,
+  activation = {},
+  address = {},
+  architecture = {No},
+  author = {Sarmento, Pedro and Kumar, Adarsh and Carr, CJ and Zukowski, Zack and Barthet, Mathieu and Yang, Yi-Hsuan},
+  batch = {},
+  booktitle = {ISMIR},
+  code = {https://github.com/dada-bots/dadaGP},
+  computationtime = {},
+  dataaugmentation = {},
+  dataset = {[DadaGP](https://drive.google.com/drive/folders/1USNH8olG9uy6vodslM3iXInBT725zult?usp=sharing)},
+  dimension = {},
+  dropout = {},
+  epochs = {},
+  framework = {No},
+  gpu = {},
+  input = {},
+  layers = {},
+  learningrate = {},
+  link = {https://archives.ismir.net/ismir2021/paper/000076.pdf},
+  loss = {},
+  metric = {},
+  momentum = {},
+  month = {Oct.},
+  note = {},
+  optimizer = {},
+  pages = {1--8},
+  reproducible = {},
+  task = {Polyphonic music sequence modelling},
+  title = {{DadaGP: a Dataset of Tokenized GuitarPro Songs for Sequence Models}},
+  year = {2021}
+}