Add some missing fields in bib

ybayle · Nov 24, 2017 · ce07d48 · ce07d48
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diff --git a/README.md b/README.md
@@ -229,16 +229,16 @@ 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.
-- 31 tasks investigated. See the list of [tasks](tasks.md).
+- 33 tasks investigated. See the list of [tasks](tasks.md).
 Tasks pie chart:
 ![Tasks pie chart](fig/pie_chart_task.png)
-- 43 datasets used. See the list of [datasets](datasets.md).
+- 47 datasets used. See the list of [datasets](datasets.md).
 Datasets pie chart:
 ![Datasets pie chart](fig/pie_chart_dataset.png)
-- 25 architectures used. See the list of [architectures](architectures.md).
+- 26 architectures used. See the list of [architectures](architectures.md).
 Architectures pie chart:
 ![Architectures pie chart](fig/pie_chart_architecture.png)
-- 8 frameworks used. See the list of [frameworks](frameworks.md).
+- 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.

diff --git a/architectures.md b/architectures.md
@@ -21,6 +21,7 @@ Please refer to the list of useful acronyms used in deep learning and music: [ac
 - MCLNN
 - MLP
 - NNMODFF
+- No
 - PNN
 - RNN
 - RNN-LSTM

diff --git a/datasets.md b/datasets.md
@@ -3,6 +3,7 @@
 Please refer to the list of useful acronyms used in deep learning and music: [acronyms.md](acronyms.md).
 
 - Inhouse
+- No
 - [32 Beethoven’s piano sonatas gathered from https://archive.org](https://soundcloud.com/samplernn/sets)
 - [7digital](https://7digital.com)
 - [ADC2004](http://labrosa.ee.columbia.edu/projects/melody/)
@@ -22,8 +23,10 @@ Please refer to the list of useful acronyms used in deep learning and music: [ac
 - [Homburg](http://www-ai.cs.uni-dortmund.de/audio.html)
 - [IDMT-SMT-Drums](https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/drums.html)
 - [IRMAS](https://www.upf.edu/web/mtg/irmas)
+- [JSB Chorales](ftp://i11ftp.ira.uka.de/pub/neuro/dominik/midifiles/bach.zip)
 - [Jamendo](http://www.mathieuramona.com/wp/data/jamendo/)
 - [LMD](https://sites.google.com/site/carlossillajr/resources/the-latin-music-database-lmd)
+- [LSDB](lsdb.flow-machines.com/)
 - [LabROSA](http://labrosa.ee.columbia.edu/projects/melody/)
 - [Lakh MIDI](https://labrosa.ee.columbia.edu/sounds/music/)
 - [Last.fm](https://www.last.fm/)
@@ -39,6 +42,7 @@ Please refer to the list of useful acronyms used in deep learning and music: [ac
 - [Piano-midi.de](Piano-midi.de)
 - [RWC](https://staff.aist.go.jp/m.goto/RWC-MDB/)
 - [SALAMI](http://ddmal.music.mcgill.ca/research/salami/annotations)
+- [Symbolic music data](http://users.cecs.anu.edu.au/~christian.walder/)
 - [TIMIT](https://catalog.ldc.upenn.edu/LDC93S1)
 - [TSP](http://www-mmsp.ece.mcgill.ca/Documents/Data/)
 - [TUT Acoustic Scenes 2016](http://www.cs.tut.fi/~mesaros/pubs/mesaros_eusipco2016-dcase.pdf)

diff --git a/dl4m.bib b/dl4m.bib
@@ -1,19 +1,20 @@
 @article{Arumugam2016,
   architecture = {PNN},
   author = {Arumugam, Muthumari and Kaliappan, Mala},
+  dataset = {[GTzan](http://marsyas.info/downloads/datasets.html)},
   journal = {[Circuits and Systems](http://www.scirp.org/journal/cs/)},
   link = {http://file.scirp.org/pdf/CS_2016042615054817.pdf},
   number = {04},
   pages = {255},
   publisher = {Scientific Research Publishing},
+  task = {MGR & Instrument recognition},
   title = {An efficient approach for segmentation, feature extraction and classification of audio signals},
   volume = {7},
   year = {2016}
 }
 
 @unpublished{Bammer2017,
   author = {Bammer, Roswitha and Doerfler, Monika},
-  journal = {arXiv preprint arXiv:1706.08818},
   link = {https://arxiv.org/pdf/1706.08818.pdf},
   title = {Gabor frames and deep scattering networks in audio processing},
   year = {2017}
@@ -39,9 +40,11 @@ @inproceedings{Bharucha1988
 }
 
 @unpublished{Briot2017,
+  architecture = {No},
   author = {Briot, Jean-Pierre and Hadjeres, Gaëtan and Pachet, François},
-  journal = {arXiv preprint arXiv:1709.01620},
+  dataset = {[JSB Chorales](ftp://i11ftp.ira.uka.de/pub/neuro/dominik/midifiles/bach.zip) & [MusicNet](https://homes.cs.washington.edu/~thickstn/musicnet.html) & [Symbolic music data](http://users.cecs.anu.edu.au/~christian.walder/) & [LSDB](lsdb.flow-machines.com/)},
   link = {https://arxiv.org/pdf/1709.01620.pdf},
+  task = {Survey & Composition},
   title = {Deep learning techniques for music generation - A survey},
   year = {2017}
 }
@@ -90,9 +93,23 @@ @unpublished{Cangea2017
 }
 
 @inproceedings{Cella2017,
+  activation = {No},
+  architecture = {No},
   author = {Cella, Carmine-Emanuele},
+  batch = {No},
   booktitle = {IWDLM},
+  code = {No},
+  dataaugmentation = {No},
+  dataset = {No},
+  dropout = {No},
+  epochs = {No},
+  framework = {No},
+  gpu = {No},
+  learningrate = {No},
   link = {http://dorienherremans.com/dlm2017/papers/cella2017mli.pdf},
+  optimizer = {No},
+  reproducible = {No},
+  task = {Manifesto},
   title = {Machine listening intelligence},
   year = {2017}
 }
@@ -258,10 +275,24 @@ @unpublished{Deng2017
 }
 
 @inproceedings{Dieleman2011,
+  activation = {Custom},
+  architecture = {CNN & MLP},
   author = {Dieleman, Sander and Brakel, Philémon and Schrauwen, Benjamin},
+  batch = {No},
   booktitle = {ISMIR},
+  code = {No},
+  dataaugmentation = {No},
+  dataset = {[MSD](https://labrosa.ee.columbia.edu/millionsong/)},
+  dropout = {0.3},
+  epochs = {1},
+  framework = {Theano},
+  gpu = {No},
+  learningrate = {0.005 & 0.0001},
   link = {http://www.ismir2011.ismir.net/papers/PS6-3.pdf},
+  optimizer = {No},
   pages = {669--674},
+  reproducible = {No},
+  task = {MGR & Artist recognition},
   title = {Audio-based music classification with a pretrained convolutional network},
   year = {2011}
 }

diff --git a/dl4m.tsv b/dl4m.tsv
@@ -1,13 +1,13 @@
 Year	Entrytype	Title	Author	Link	Code	Task	Reproducible	Dataset	Framework	Architecture	Dropout	Batch	Epochs	Dataaugmentation	Input	Dimension	Activation	Loss	Learningrate	Optimizer	Gpu	
-2016	article	An efficient approach for segmentation, feature extraction and classification of audio signals	Arumugam, Muthumari and Kaliappan, Mala	http://file.scirp.org/pdf/CS_2016042615054817.pdf						PNN												
+2016	article	An efficient approach for segmentation, feature extraction and classification of audio signals	Arumugam, Muthumari and Kaliappan, Mala	http://file.scirp.org/pdf/CS_2016042615054817.pdf		MGR & Instrument recognition		[GTzan](http://marsyas.info/downloads/datasets.html)		PNN												
 2017	unpublished	Gabor frames and deep scattering networks in audio processing	Bammer, Roswitha and Doerfler, Monika	https://arxiv.org/pdf/1706.08818.pdf																		
 2017	inproceedings	Vision-based detection of acoustic timed events: A case study on clarinet note onsets	Bazzica, Alessio and Van Gemert, JC and Liem, CCS and Hanjalic, A	http://dorienherremans.com/dlm2017/papers/bazzica2017clarinet.pdf		Onset detection		[C4S](http://mmc.tudelft.nl/users/alessio-bazzica#C4S-dataset)		CNN												
 1988	inproceedings	Neural net modeling of music	Bharucha, J.																			
-2017	unpublished	Deep learning techniques for music generation - A survey	Briot, Jean-Pierre and Hadjeres, Gaëtan and Pachet, François	https://arxiv.org/pdf/1709.01620.pdf																		
+2017	unpublished	Deep learning techniques for music generation - A survey	Briot, Jean-Pierre and Hadjeres, Gaëtan and Pachet, François	https://arxiv.org/pdf/1709.01620.pdf		Survey & Composition		[JSB Chorales](ftp://i11ftp.ira.uka.de/pub/neuro/dominik/midifiles/bach.zip) & [MusicNet](https://homes.cs.washington.edu/~thickstn/musicnet.html) & [Symbolic music data](http://users.cecs.anu.edu.au/~christian.walder/) & [LSDB](lsdb.flow-machines.com/)		No												
 2017	inproceedings	JamBot: Music theory aware chord based generation of polyphonic music with LSTMs	Brunner, Gino and Wang, Yuyi and Wattenhofer, Roger and Wiesendanger, Jonas	https://arxiv.org/pdf/1711.07682.pdf	https://github.com/brunnergino/JamBot	Composition		[Lakh MIDI](https://labrosa.ee.columbia.edu/sounds/music/)	Keras-TensorFlow	RNN-LSTM	No	No	4	No			Softmax		0.00001	Adam	1	
 2002	inproceedings	A supervised learning approach to musical style recognition	Buzzanca, Giuseppe	https://www.researchgate.net/profile/Giuseppe_Buzzanca/publication/228588086_A_supervised_learning_approach_to_musical_style_recognition/links/54b43ee90cf26833efd0109f.pdf		MGR																
 2017	unpublished	XFlow: 1D <-> 2D cross-modal deep neural networks for audiovisual classification	Cangea, Cătălina and Veličković, Petar and Liò, Pietro	https://arxiv.org/pdf/1709.00572.pdf																		
-2017	inproceedings	Machine listening intelligence	Cella, Carmine-Emanuele	http://dorienherremans.com/dlm2017/papers/cella2017mli.pdf																		
+2017	inproceedings	Machine listening intelligence	Cella, Carmine-Emanuele	http://dorienherremans.com/dlm2017/papers/cella2017mli.pdf	No	Manifesto	No	No	No	No	No	No	No	No			No		No	No	No	
 2017	inproceedings	Deep multimodal network for multi-label classification	Chen, Tanfang and Wang, Shangfei and Chen, Shiyu	http://ieeexplore.ieee.org/abstract/document/8019322/		General audio classification																
 2017	unpublished	A tutorial on deep learning for music information retrieval	Choi, Keunwoo and Fazekas, György and Cho, Kyunghyun and Sandler, Mark Brian	https://arxiv.org/pdf/1709.04396.pdf	https://github.com/keunwoochoi/dl4mir	General audio classification																
 2017	unpublished	A comparison on audio signal preprocessing methods for deep neural networks on music tagging	Choi, Keunwoo and Fazekas, György and Cho, Kyunghyun and Sandler, Mark Brian	https://arxiv.org/pdf/1709.01922.pdf	https://github.com/keunwoochoi/transfer_learning_music	MGR		[MSD](https://labrosa.ee.columbia.edu/millionsong/)														
@@ -22,7 +22,7 @@ Year	Entrytype	Title	Author	Link	Code	Task	Reproducible	Dataset	Framework	Archit
 1997	inproceedings	A machine learning approach to musical style recognition	Dannenberg, Roger B and Thom, Belinda and Watson, David	http://repository.cmu.edu/cgi/viewcontent.cgi?article=1496&context=compsci		MSR																
 2016	inproceedings	Automatic chord estimation on seventhsbass chord vocabulary using deep neural network	Deng, Junqi and Kwok, Yu-Kwong	http://ieeexplore.ieee.org/abstract/document/7471677/		Chord recognition																
 2017	unpublished	Large vocabulary automatic chord estimation using deep neural nets: Design framework, system variations and limitations	Deng, Junqi and Kwok, Yu-Kwong	https://arxiv.org/pdf/1709.07153.pdf		Chord recognition																
-2011	inproceedings	Audio-based music classification with a pretrained convolutional network	Dieleman, Sander and Brakel, Philémon and Schrauwen, Benjamin	http://www.ismir2011.ismir.net/papers/PS6-3.pdf																		
+2011	inproceedings	Audio-based music classification with a pretrained convolutional network	Dieleman, Sander and Brakel, Philémon and Schrauwen, Benjamin	http://www.ismir2011.ismir.net/papers/PS6-3.pdf	No	MGR & Artist recognition	No	[MSD](https://labrosa.ee.columbia.edu/millionsong/)	Theano	CNN & MLP	0.3	No	1	No			Custom		0.005 & 0.0001	No	No	
 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		

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/frameworks.md b/frameworks.md
@@ -6,6 +6,7 @@ Please refer to the list of useful acronyms used in deep learning and music: [ac
 - Keras
 - Keras-TensorFlow
 - Keras-Theano
+- No
 - Not disclosed
 - PyTorch
 - Tensorflow

diff --git a/tasks.md b/tasks.md
@@ -16,6 +16,7 @@ Please refer to the list of useful acronyms used in deep learning and music: [ac
 - MER
 - MGR
 - MSR
+- Manifesto
 - Mixing
 - Music/Noise segmentation
 - Noise suppression
@@ -30,6 +31,7 @@ Please refer to the list of useful acronyms used in deep learning and music: [ac
 - SVS
 - Source separation
 - Speaker gender recognition
+- Survey
 - Syllable segmentation
 - Transcription
 - VAD
-Original file line number
+Diff line change
@@ Expand Up @@
     - MCLNN
     - MLP
     - NNMODFF
+    - No
     - PNN
     - RNN
     - RNN-LSTM
@@ Expand Down @@