Add Dong2018

ybayle · Nov 28, 2017 · 19baac7 · 19baac7
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diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -70,6 +70,7 @@ Here are the steps to follow for adding one (or multiple) article:
         note = {},
         optimizer = {},
         pages = {},
+        reproducible = {},
         task = {},
         title = {},
         year = {},

diff --git a/README.md b/README.md
@@ -53,6 +53,7 @@ However, these surveys do not cover music information retrieval tasks that are i
 | [Multiscale approaches to music audio feature learning](http://ismir2013.ismir.net/wp-content/uploads/2013/09/69_Paper.pdf) | No |
 | [End-to-end learning for music audio](http://ieeexplore.ieee.org/abstract/document/6854950/) | No |
 | [Basic filters for convolutional neural networks: Training or design?](https://arxiv.org/pdf/1709.02291.pdf) | No |
+| [MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment](https://arxiv.org/pdf/1709.06298.pdf) | [GitHub](https://github.com/salu133445/musegan) |
 | [Ensemble Of Deep Neural Networks For Acoustic Scene Classification](https://arxiv.org/pdf/1708.05826.pdf) | No |
 | [Robust downbeat tracking using an ensemble of convolutional networks](http://ieeexplore.ieee.org/abstract/document/7728057/) | No |
 | [Downbeat tracking with multiple features and deep neural networks](http://perso.telecom-paristech.fr/~grichard/Publications/2015-durand-icassp.pdf) | No |
@@ -226,24 +227,24 @@ Each entry in [dl4m.bib](dl4m.bib) also displays additional information:
 
 ## Statistics and visualisations
 
-- 155 papers referenced. See the details in [dl4m.bib](dl4m.bib).
+- 156 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 [323 other researchers](authors.md) in your field.
+- If you are applying DL to music, there are [326 other researchers](authors.md) in your field.
 - 33 tasks investigated. See the list of [tasks](tasks.md).
 Tasks pie chart:
 ![Tasks pie chart](fig/pie_chart_task.png)
-- 47 datasets used. See the list of [datasets](datasets.md).
+- 48 datasets used. See the list of [datasets](datasets.md).
 Datasets pie chart:
 ![Datasets pie chart](fig/pie_chart_dataset.png)
-- 26 architectures used. See the list of [architectures](architectures.md).
+- 27 architectures used. See the list of [architectures](architectures.md).
 Architectures pie chart:
 ![Architectures pie chart](fig/pie_chart_architecture.png)
 - 9 frameworks used. See the list of [frameworks](frameworks.md).
 Frameworks pie chart:
 ![Frameworks pie chart](fig/pie_chart_framework.png)
-- Only 39 articles (25%) provide their source code.
+- Only 40 articles (25%) 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-)

diff --git a/architectures.md b/architectures.md
@@ -17,6 +17,7 @@ Please refer to the list of useful acronyms used in deep learning and music: [ac
 - DNN
 - ELM
 - FCN
+- GAN
 - HAN
 - MCLNN
 - MLP

diff --git a/authors.md b/authors.md
@@ -49,6 +49,7 @@
 - Dimoulas, Charalampos
 - Dixon, Simon
 - Doerfler, Monika
+- Dong, Hao-Wen
 - Dorfer, Matthias
 - Drossos, Konstantinos
 - Duppada, Venkatesh
@@ -100,6 +101,7 @@
 - Hirvonen, Toni
 - Hockman, Jason
 - Holzapfel, Andre
+- Hsiao, Wen-Yi
 - Hsu, Yu-Lun
 - Hu, Min-Chun
 - Huang, Allen
@@ -311,6 +313,7 @@
 - Xu, Mingxing
 - Xu, Yong
 - Yan, Yonghong
+- Yang, Li-Chia
 - Yang, Yi-Hsuan
 - Ycart, Adrien
 - Yong Xu

diff --git a/datasets.md b/datasets.md
@@ -29,6 +29,7 @@ Please refer to the list of useful acronyms used in deep learning and music: [ac
 - [LSDB](lsdb.flow-machines.com/)
 - [LabROSA](http://labrosa.ee.columbia.edu/projects/melody/)
 - [Lakh MIDI](https://labrosa.ee.columbia.edu/sounds/music/)
+- [Lakh Pianoroll Datase](https://github.com/salu133445/musegan/blob/master/docs/dataset.md)
 - [Last.fm](https://www.last.fm/)
 - [LyricFind](http://lyricfind.com/)
 - [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/)

diff --git a/dl4m.bib b/dl4m.bib
@@ -336,6 +336,37 @@ @unpublished{Doerfler2017
   year = {2017}
 }
 
+@inproceedings{Dong2018,
+  activation = {ReLU & Leaky ReLU},
+  architecture = {GAN & CNN},
+  author = {Dong, Hao-Wen and Hsiao, Wen-Yi and Yang, Li-Chia and Yang, Yi-Hsuan},
+  batch = {No},
+  booktitle = {AAAI},
+  code = {https://github.com/salu133445/musegan},
+  computationtime = {24 hours},
+  dataaugmentation = {No},
+  dataset = {[Lakh Pianoroll Datase](https://github.com/salu133445/musegan/blob/master/docs/dataset.md)},
+  dimension = {1D},
+  dropout = {No},
+  epochs = {No},
+  framework = {No},
+  gpu = {1 Tesla K40m},
+  input = {Piano-roll},
+  layers = {9},
+  learningrate = {No},
+  link = {https://arxiv.org/pdf/1709.06298.pdf},
+  loss = {No},
+  metric = {No},
+  month = {Nov.},
+  note = {5 metrics proposed for evaluation},
+  optimizer = {Adam},
+  pages = {1--13},
+  reproducible = {No},
+  task = {Composition},
+  title = {MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment},
+  year = {2018}
+}
+
 @unpublished{Duppada2017,
   author = {Duppada, Venkatesh and Hiray, Sushant},
   journal = {arXiv preprint arXiv:1708.05826},

diff --git a/dl4m.tsv b/dl4m.tsv
@@ -26,6 +26,7 @@ Year	Entrytype	Title	Author	Link	Code	Task	Reproducible	Dataset	Framework	Archit
 2013	inproceedings	Multiscale approaches to music audio feature learning	Dieleman, Sander and Schrauwen, Benjamin	http://ismir2013.ismir.net/wp-content/uploads/2013/09/69_Paper.pdf				[Magnatagatune](http://mirg.city.ac.uk/codeapps/the-magnatagatune-dataset)							Mel-spectrogram			cross-entropy				
 2014	inproceedings	End-to-end learning for music audio	Dieleman, Sander and Schrauwen, Benjamin	http://ieeexplore.ieee.org/abstract/document/6854950/		MGR		[Magnatagatune](http://mirg.city.ac.uk/codeapps/the-magnatagatune-dataset)		CNN					Raw & Mel-spectrogram							
 2017	unpublished	Basic filters for convolutional neural networks: Training or design?	Doerfler, Monika and Grill, Thomas and Bammer, Roswitha and Flexer, Arthur	https://arxiv.org/pdf/1709.02291.pdf		SVD		Inhouse							Raw & Mel-spectrogram				0.001	Adam		
+2018	inproceedings	MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment	Dong, Hao-Wen and Hsiao, Wen-Yi and Yang, Li-Chia and Yang, Yi-Hsuan	https://arxiv.org/pdf/1709.06298.pdf	https://github.com/salu133445/musegan	Composition	No	[Lakh Pianoroll Datase](https://github.com/salu133445/musegan/blob/master/docs/dataset.md)	No	GAN & CNN	No	No	No	No	Piano-roll	1D	ReLU & Leaky ReLU	No	No	Adam	1 Tesla K40m	
 2017	unpublished	Ensemble Of Deep Neural Networks For Acoustic Scene Classification	Duppada, Venkatesh and Hiray, Sushant	https://arxiv.org/pdf/1708.05826.pdf																		
 2017	article	Robust downbeat tracking using an ensemble of convolutional networks	Durand, Simon and Bello, Juan Pablo and David, Bertrand and Richard, Gaël	http://ieeexplore.ieee.org/abstract/document/7728057/		Beat detection				CNN												
 2015	inproceedings	Downbeat tracking with multiple features and deep neural networks	Durand, Simon and Bello, Juan Pablo and David, Bertrand and Richard, Gaël	http://perso.telecom-paristech.fr/~grichard/Publications/2015-durand-icassp.pdf		Beat detection																

diff --git a/fig/articles_per_year.png b/fig/articles_per_year.png
diff --git a/fig/pie_chart_architecture.png b/fig/pie_chart_architecture.png
diff --git a/fig/pie_chart_dataset.png b/fig/pie_chart_dataset.png
diff --git a/fig/pie_chart_framework.png b/fig/pie_chart_framework.png
diff --git a/fig/pie_chart_task.png b/fig/pie_chart_task.png
diff --git a/publication_type.md b/publication_type.md
@@ -16,6 +16,7 @@
 
 ### Conferences:
 
+- AAAI
 - ACM_MM
 - Audio Engineering Society Convention
 - Biennial Symposium for Arts and Technology
-Original file line number
+Diff line change
@@ Expand Up @@
     - DNN
     - ELM
     - FCN
+    - GAN
     - HAN
     - MCLNN
     - MLP
@@ Expand Down @@