add Eck2002

ybayle · Jan 17, 2018 · f8e3f78 · f8e3f78
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Showing 12 changed files with 50 additions and 5 deletions.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -32,9 +32,10 @@ Here are the steps to follow for adding one (or multiple) article:
     - loss (if given in the paper or the code, the loss function, otherwise `No`)
     - layers (if given in the paper or the code, the number of layers, otherwise `No`)
     - dropout (if given in the paper or the code, the loss function, otherwise `No`)
-    - gpu (if given in the paper or the code, the loss function, otherwise `No`)
-    - metric (if given in the paper or the code, the loss function, otherwise `No`)
-    - computationtime (if given in the paper or the code, the loss function, otherwise `No`)
+    - momentum (if given in the paper or the code, the momentum, otherwise `No`)
+    - gpu (if given in the paper or the code, the type and number of GPUs, otherwise `No`)
+    - metric (if given in the paper or the code, the metric, otherwise `No`)
+    - computationtime (if given in the paper or the code, the global computation time and per epoch, otherwise `No`)
     - dimension (if given in the paper or the code, the number of dimension, otherwise `No`)
     - optimizer (if given in the paper or the code, the optimize function, otherwise `No`)
     - input (if given in the paper or the code, the input type, otherwise `No`)
@@ -66,6 +67,7 @@ Here are the steps to follow for adding one (or multiple) article:
         link = {},
         loss = {},
         metric = {},
+        momentum = {},
         month = {},
         note = {},
         optimizer = {},

diff --git a/README.md b/README.md
@@ -58,6 +58,7 @@ However, these surveys do not cover music information retrieval tasks that are i
 | [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 |
+| [Finding temporal structure in music: Blues improvisation with LSTM recurrent networks](ftp://ftp.idsia.ch/pub/juergen/2002_ieee.pdf) | No |
 | [Music signal processing using vector product neural networks](http://dorienherremans.com/dlm2017/papers/fan2017vector.pdf) | No |
 | [Deep learning for music genre classification](https://courses.engr.illinois.edu/ece544na/fa2014/Tao_Feng.pdf) | No |
 | [Multi-phase learning for jazz improvisation and interaction](http://www.cs.smith.edu/~jfrankli/papers/CtColl01.pdf) | No |
@@ -229,11 +230,11 @@ Each entry in [dl4m.bib](dl4m.bib) also displays additional information:
 
 ## Statistics and visualisations
 
-- 156 papers referenced. See the details in [dl4m.bib](dl4m.bib).
+- 157 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 [326 other researchers](authors.md) in your field.
+- If you are applying DL to music, there are [328 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)

diff --git a/acronyms.md b/acronyms.md
@@ -10,8 +10,10 @@ A list of useful acronyms used in deep learning and music sorted alphabetically.
 | ANN | Artificial Neural Network |
 | ARNN | Anticipation Recurrent Neural Network |
 | BILSTM | Bidirectional Long Short-Term Memory |
+| BPTT | Back-Propagation Through Time |
 | BRNN | Bidirectional Recurrent Neural Network |
 | CDBN | Convolutional Deep Belief Networks |
+| CEC | Constant Error Carousel |
 | CLNN | ConditionaL Neural Networks |
 | CNN | Convolutional Neural Network |
 | ConvNet | Convolutional Neural Network |
@@ -54,8 +56,10 @@ A list of useful acronyms used in deep learning and music sorted alphabetically.
 | ReLU | Rectified Linear Unit |
 | RICNN | Rotation Invariant Convolutional Neural Network |
 | RNN | Recurrent Neural Network |
+| RTRL | Real-Time Recurrent Learning |
 | SAE | Stacked AE |
 | SDAE | Stacked DAE |
+| SGD | Stochastic Gradient Descent |
 | SVM | Support Vector Machine |
 | SVD | Singing Voice Detection |
 | SVS | Singing Voice Separation |

diff --git a/authors.md b/authors.md
@@ -54,6 +54,7 @@
 - Drossos, Konstantinos
 - Duppada, Venkatesh
 - Durand, Simon
+- Eck, Douglas
 - Ehmann, Andreas F.
 - Ellis, Daniel P. W.
 - Emilia Gomez
@@ -240,6 +241,7 @@
 - Schedl, Markus
 - Scherer, Klaus R
 - Schlüter, Jan
+- Schmidhuber, Juergen
 - Schmidt, Erik M.
 - Schrauwen, Benjamin
 - Schuller, Björn W

diff --git a/dl4m.bib b/dl4m.bib
@@ -400,6 +400,40 @@ @inproceedings{Durand2015
   year = {2015}
 }
 
+@inproceedings{Eck2002,
+  activation = {Logistic Sigmoid},
+  address = {Martigny, Valais, Switzerland},
+  architecture = {RNN-LSTM},
+  author = {Eck, Douglas and Schmidhuber, Juergen},
+  batch = {No},
+  booktitle = {[NNSP](http://cogsys.imm.dtu.dk/nnsp2002/)},
+  code = {No},
+  computationtime = {15-45 min 1Ghz Pentium},
+  dataaugmentation = {No},
+  dataset = {Inhouse},
+  dimension = {1D},
+  dropout = {No},
+  epochs = {No},
+  framework = {No},
+  gpu = {No},
+  input = {Midi Chords & Midi notes},
+  layers = {1},
+  learningrate = {0.00001},
+  link = {ftp://ftp.idsia.ch/pub/juergen/2002_ieee.pdf},
+  loss = {cross-entropy},
+  metric = {cross-entropy},
+  momentum = {0.9},
+  month = {Sep.},
+  note = {},
+  optimizer = {SGD},
+  organization = {IEEE},
+  pages = {747--756},
+  reproducible = {No},
+  task = {Composition},
+  title = {Finding temporal structure in music: Blues improvisation with LSTM recurrent networks},
+  year = {2002}
+}
+
 @inproceedings{Fan2017,
   architecture = {VPNN & DNN},
   author = {Fan, Zhe-Cheng and Chan, TS and Yang, Yi-Hsuan and Jang, Jyh-Shing R},

diff --git a/dl4m.tsv b/dl4m.tsv
@@ -30,6 +30,7 @@ Year	Entrytype	Title	Author	Link	Code	Task	Reproducible	Dataset	Framework	Archit
 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																
+2002	inproceedings	Finding temporal structure in music: Blues improvisation with LSTM recurrent networks	Eck, Douglas and Schmidhuber, Juergen	ftp://ftp.idsia.ch/pub/juergen/2002_ieee.pdf	No	Composition	No	Inhouse	No	RNN-LSTM	No	No	No	No	Midi Chords & Midi notes	1D	Logistic Sigmoid	cross-entropy	0.00001	SGD	No	
 2017	inproceedings	Music signal processing using vector product neural networks	Fan, Zhe-Cheng and Chan, TS and Yang, Yi-Hsuan and Jang, Jyh-Shing R	http://dorienherremans.com/dlm2017/papers/fan2017vector.pdf		SVS		[iKala](http://mac.citi.sinica.edu.tw/ikala/)		VPNN & DNN												
 2014	techreport	Deep learning for music genre classification	Feng, Tao	https://courses.engr.illinois.edu/ece544na/fa2014/Tao_Feng.pdf		MGR		[GTzan](http://marsyas.info/downloads/datasets.html)														
 2001	inproceedings	Multi-phase learning for jazz improvisation and interaction	Franklin, Judy A	http://www.cs.smith.edu/~jfrankli/papers/CtColl01.pdf		Composition				RNN												

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
@@ -56,3 +56,4 @@
 - [IEEE_ICTAI](http://ictai2017.org/)
 - [NIPS](https://nips.cc/)
 - [NIPS_ML4Audio](https://nips.cc/Conferences/2017/Schedule?showEvent=8790)
+- [NNSP](http://cogsys.imm.dtu.dk/nnsp2002/)