Marolt2002

ybayle · Jan 17, 2018 · 4e7d3e0 · 4e7d3e0
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diff --git a/README.md b/README.md
@@ -110,6 +110,7 @@ However, these surveys do not cover music information retrieval tasks that are i
 | [Event localization in music auto-tagging](http://mac.citi.sinica.edu.tw/~yang/pub/liu16mm.pdf) | [GitHub](https://github.com/ciaua/clip2frame) |
 | [Deep convolutional networks on the pitch spiral for musical instrument recognition](https://github.com/lostanlen/ismir2016/blob/master/paper/lostanlen_ismir2016.pdf) | [GitHub](https://github.com/lostanlen/ismir2016) |
 | [Stacked convolutional and recurrent neural networks for music emotion recognition](https://arxiv.org/pdf/1706.02292.pdf) | No |
+| [Neural networks for note onset detection in piano music](https://www.researchgate.net/profile/Matija_Marolt/publication/2473938_Neural_Networks_for_Note_Onset_Detection_in_Piano_Music/links/00b49525efccc79fed000000.pdf) | No |
 | [A deep learning approach to source separation and remixing of hiphop music](https://repositori.upf.edu/bitstream/handle/10230/32919/Martel_2017.pdf?sequence=1&isAllowed=y) | No |
 | [Neural network based model for classification of music type](http://ieeexplore.ieee.org/abstract/document/514161/) | No |
 | [A software framework for musical data augmentation](https://bmcfee.github.io/papers/ismir2015_augmentation.pdf) | No |
@@ -230,11 +231,11 @@ Each entry in [dl4m.bib](dl4m.bib) also displays additional information:
 
 ## Statistics and visualisations
 
-- 157 papers referenced. See the details in [dl4m.bib](dl4m.bib).
+- 158 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 [328 other researchers](authors.md) in your field.
+- If you are applying DL to music, there are [331 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)
@@ -247,7 +248,7 @@ 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 41 articles (26%) provide their source code.
+- Only 41 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/authors.md b/authors.md
@@ -125,6 +125,7 @@
 - Jordi Janer
 - Kakade, Sham M.
 - Kaliappan, Mala
+- Kavcic, Alenka
 - Keefe, Douglas H.
 - Kelz, Rainer
 - Kereliuk, Corey
@@ -174,6 +175,7 @@
 - Malik, Miroslav
 - Marius Miron
 - Mark D. Plumbley
+- Marolt, Matija
 - Martel Baro, Héctor
 - Mathulaprangsan, Seksan
 - Matityaho, Benyamin
@@ -219,6 +221,7 @@
 - Poggio, Tomaso
 - Pons, Jordi
 - Pritish Chandna
+- Privosnik, Marko
 - Prockup, Matthew
 - Qian, Jiyuan
 - Qian, Sheng

diff --git a/dl4m.bib b/dl4m.bib
@@ -1004,6 +1004,37 @@ @unpublished{Malik2017
   year = {2017}
 }
 
+@unpublished{Marolt2002,
+  activation = {No},
+  address = {Gothenburg, Sweden},
+  architecture = {MLP},
+  author = {Marolt, Matija and Kavcic, Alenka and Privosnik, Marko},
+  batch = {No},
+  code = {No},
+  computationtime = {No},
+  dataaugmentation = {No},
+  dataset = {Inhouse},
+  dimension = {1D},
+  dropout = {No},
+  epochs = {No},
+  framework = {No},
+  gpu = {No},
+  input = {Raw audio signal and synthesized},
+  layers = {1},
+  learningrate = {No},
+  link = {https://www.researchgate.net/profile/Matija_Marolt/publication/2473938_Neural_Networks_for_Note_Onset_Detection_in_Piano_Music/links/00b49525efccc79fed000000.pdf},
+  loss = {No},
+  metric = {No},
+  month = {Sep.},
+  note = {One should take care when citing this article as it is referenced to be published in ICMC 2002 (cf https://scholar.google.fr/scholar?hl=fr&as_sdt=0%2C5&q=Neural+Networks+for+Note+Onset+Detection+in+Piano+Music&btnG=) but it is not in the proceedings of this conference (cf http://dblp.uni-trier.de/db/conf/icmc/icmc2002). If you have more info please get in touch.},
+  optimizer = {No},
+  pages = {1--4},
+  reproducible = {No},
+  task = {Onset detection},
+  title = {Neural networks for note onset detection in piano music},
+  year = {2002}
+}
+
 @mastersthesis{Martelbaro2017,
   architecture = {DNN & CNN & RNN},
   author = {Martel Baro, Héctor},

diff --git a/dl4m.tsv b/dl4m.tsv
@@ -82,6 +82,7 @@ Year	Entrytype	Title	Author	Link	Code	Task	Reproducible	Dataset	Framework	Archit
 2016	inproceedings	Event localization in music auto-tagging	Liu, Jen-Yu and Yang, Yi-Hsuan	http://mac.citi.sinica.edu.tw/~yang/pub/liu16mm.pdf	https://github.com/ciaua/clip2frame					CNN												
 2016	inproceedings	Deep convolutional networks on the pitch spiral for musical instrument recognition	Lostanlen, Vincent and Cella, Carmine-Emanuele	https://github.com/lostanlen/ismir2016/blob/master/paper/lostanlen_ismir2016.pdf	https://github.com/lostanlen/ismir2016	Instrument recognition																
 2017	unpublished	Stacked convolutional and recurrent neural networks for music emotion recognition	Malik, Miroslav and Adavanne, Sharath and Drossos, Konstantinos and Virtanen, Tuomas and Ticha, Dasa and Jarina, Roman	https://arxiv.org/pdf/1706.02292.pdf	No	MER		[Free music archive](http://freemusicarchive.org/) & [MedleyDB](http://medleydb.weebly.com/) & [Jamendo](http://www.mathieuramona.com/wp/data/jamendo/)	Keras-Theano	CRNN				No				RMSE		Adam		
+2002	unpublished	Neural networks for note onset detection in piano music	Marolt, Matija and Kavcic, Alenka and Privosnik, Marko	https://www.researchgate.net/profile/Matija_Marolt/publication/2473938_Neural_Networks_for_Note_Onset_Detection_in_Piano_Music/links/00b49525efccc79fed000000.pdf	No	Onset detection	No	Inhouse	No	MLP	No	No	No	No	Raw audio signal and synthesized	1D	No	No	No	No	No	
 2017	mastersthesis	A deep learning approach to source separation and remixing of hiphop music	Martel Baro, Héctor	https://repositori.upf.edu/bitstream/handle/10230/32919/Martel_2017.pdf?sequence=1&isAllowed=y		Source separation & Remixing		[DSD100](http://sisec17.audiolabs-erlangen.de/#/dataset) & [HHDS](https://drive.google.com/drive/folders/0B1zpiGdDzFNlbmJyYU1VVFR3OEE)		DNN & CNN & RNN				Mixing & Circular Shift & Instrument augmentation								
 1995	inproceedings	Neural network based model for classification of music type	Matityaho, Benyamin and Furst, Miriam	http://ieeexplore.ieee.org/abstract/document/514161/		MGR																
 2015	inproceedings	A software framework for musical data augmentation	McFee, Brian and Humphrey, Eric J. and Bello, Juan Pablo	https://bmcfee.github.io/papers/ismir2015_augmentation.pdf		Instrument recognition		[MedleyDB](http://medleydb.weebly.com/)														

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/reproducibility.md b/reproducibility.md
@@ -1,6 +1,6 @@
 # Reproducibility and replicability
 
-Out of the 156 papers listed in this repository, only 41 articles provide their source code.
+Out of the 158 papers listed in this repository, only 41 articles provide their source code.
 Repeatability is the key to good science.
 Below is a list of useful links for reproducibility and replicability in MIR and ML: