add Kaminskyj & Materka 1995 #5

README.md

@@ -39,6 +39,7 @@ However, these surveys do not cover music information retrieval tasks that are i
 | 1989 | [Algorithms for music composition by neural nets: Improved CBR paradigms](https://quod.lib.umich.edu/cgi/p/pod/dod-idx/algorithms-for-music-composition.pdf?c=icmc;idno=bbp2372.1989.044;format=pdf) | No |
 | 1989 | [A connectionist approach to algorithmic composition](http://www.jstor.org/stable/3679551) | No |
 | 1994 | [Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing](http://www-labs.iro.umontreal.ca/~pift6080/H09/documents/papers/mozer-music.pdf) | No |
+| 1995 | [Automatic source identification of monophonic musical instrument sounds](https://www.researchgate.net/publication/3622871_Automatic_source_identification_of_monophonic_musical_instrument_sounds) | No |
 | 1995 | [Neural network based model for classification of music type](http://ieeexplore.ieee.org/abstract/document/514161/) | No |
 | 1997 | [A machine learning approach to musical style recognition](http://repository.cmu.edu/cgi/viewcontent.cgi?article=1496&context=compsci) | No |
 | 1998 | [Recognition of music types](https://www.ri.cmu.edu/pub_files/pub1/soltau_hagen_1998_2/soltau_hagen_1998_2.pdf) | No |
@@ -70,7 +71,7 @@ However, these surveys do not cover music information retrieval tasks that are i
 | 2014 | [Boundary detection in music structure analysis using convolutional neural networks](https://dav.grrrr.org/public/pub/ullrich_schlueter_grill-2014-ismir.pdf) | No |
 | 2014 | [Improving content-based and hybrid music recommendation using deep learning](http://www.smcnus.org/wp-content/uploads/2014/08/reco_MM14.pdf) | No |
 | 2014 | [A deep representation for invariance and music classification](http://www.mirlab.org/conference_papers/International_Conference/ICASSP%202014/papers/p7034-zhang.pdf) | No |
-| 2015 | [Auralisation of deep convolutional neural networks: Listening to learned features](http://ismir2015.uma.es/LBD/LBD24.pdf) | [Github](https://github.com/keunwoochoi/Auralisation) |
+| 2015 | [Auralisation of deep convolutional neural networks: Listening to learned features](http://ismir2015.uma.es/LBD/LBD24.pdf) | [GitHub](https://github.com/keunwoochoi/Auralisation) |
 | 2015 | [Downbeat tracking with multiple features and deep neural networks](http://perso.telecom-paristech.fr/~grichard/Publications/2015-durand-icassp.pdf) | No |
 | 2015 | [Music boundary detection using neural networks on spectrograms and self-similarity lag matrices](http://www.ofai.at/~jan.schlueter/pubs/2015_eusipco.pdf) | No |
 | 2015 | [Classification of spatial audio location and content using convolutional neural networks](https://www.researchgate.net/profile/Toni_Hirvonen/publication/276061831_Classification_of_Spatial_Audio_Location_and_Content_Using_Convolutional_Neural_Networks/links/5550665908ae12808b37fe5a/Classification-of-Spatial-Audio-Location-and-Content-Using-Convolutional-Neural-Networks.pdf) | No |
@@ -237,11 +238,11 @@ Each entry in [dl4m.bib](dl4m.bib) also displays additional information:
 
 ## Statistics and visualisations
 
-- 159 papers referenced. See the details in [dl4m.bib](dl4m.bib).
+- 160 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 [327 other researchers](authors.md) in your field.
+- If you are applying DL to music, there are [329 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)
@@ -254,7 +255,7 @@ 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 (25%) provide their source code.
+- Only 42 articles (26%) 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-)

authors.md

@@ -124,6 +124,7 @@
 - Jeong, Il-Young
 - Kakade, Sham M.
 - Kaliappan, Mala
+- Kaminsky, I.
 - Kavcic, Alenka
 - Keefe, Douglas H.
 - Kelz, Rainer
@@ -174,6 +175,7 @@
 - Malik, Miroslav
 - Marolt, Matija
 - Martel Baro, Héctor
+- Materka, Andrzej
 - Mathulaprangsan, Seksan
 - Matityaho, Benyamin
 - McFee, Brian

dl4m.bib

@@ -79,6 +79,40 @@ @article{Mozer1999
   year = {1994}
 }
 
+@inproceedings{Kaminsky1995,
+  activation = {Sigmoid},
+  address = {Perth, WA, Australia, Australia},
+  architecture = {No},
+  author = {Kaminsky, I. and Materka, Andrzej},
+  batch = {No},
+  booktitle = {IEEE_ICNN},
+  code = {No},
+  computationtime = {No},
+  dataaugmentation = {No},
+  dataset = {Inhouse},
+  dimension = {1D},
+  doi = {10.1109/ICNN.1995.488091},
+  dropout = {No},
+  epochs = {No},
+  framework = {No},
+  gpu = {No},
+  input = {Raw audio},
+  layers = {1},
+  learningrate = {0.25},
+  link = {https://www.researchgate.net/publication/3622871_Automatic_source_identification_of_monophonic_musical_instrument_sounds},
+  loss = {No},
+  metric = {No},
+  momentum = {0.15},
+  month = {Nov.},
+  note = {https://ieeexplore.ieee.org/document/488091},
+  optimizer = {No},
+  pages = {189-194 vol.1},
+  reproducible = {No},
+  task = {Instrument recognition},
+  title = {Automatic source identification of monophonic musical instrument sounds},
+  year = {1995}
+}
+
 @inproceedings{Matityaho1995,
   address = {Israel},
   author = {Matityaho, Benyamin and Furst, Miriam},
@@ -523,6 +557,7 @@ @inproceedings{Zhang2014
 @inproceedings{Choi2015,
   author = {Choi, Keunwoo and Fazekas, György and Sandler, Mark Brian and Kim, Jeonghee},
   booktitle = {ISMIR},
+  code = {https://github.com/keunwoochoi/Auralisation},
   dataset = {Inhouse},
   input = {STFT},
   link = {http://ismir2015.uma.es/LBD/LBD24.pdf},

dl4m.tsv

@@ -6,6 +6,7 @@ Year	Entrytype	Title	Author	Link	Code	Task	Reproducible	Dataset	Framework	Archit
 1989	inproceedings	Algorithms for music composition by neural nets: Improved CBR paradigms	Lewis, J. P.	https://quod.lib.umich.edu/cgi/p/pod/dod-idx/algorithms-for-music-composition.pdf?c=icmc;idno=bbp2372.1989.044;format=pdf		Composition																
 1989	article	A connectionist approach to algorithmic composition	Todd, Peter M.	http://www.jstor.org/stable/3679551		Composition																
 1994	article	Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing	Mozer, Michael C.	http://www-labs.iro.umontreal.ca/~pift6080/H09/documents/papers/mozer-music.pdf		Composition																
+1995	inproceedings	Automatic source identification of monophonic musical instrument sounds	Kaminsky, I. and Materka, Andrzej	https://www.researchgate.net/publication/3622871_Automatic_source_identification_of_monophonic_musical_instrument_sounds	No	Instrument recognition	No	Inhouse	No	No	No	No	No	No	Raw audio	1D	Sigmoid	No	0.25	No	No	
 1995	inproceedings	Neural network based model for classification of music type	Matityaho, Benyamin and Furst, Miriam	http://ieeexplore.ieee.org/abstract/document/514161/		MGR																
 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																
 1998	inproceedings	Recognition of music types	Soltau, Hagen and Schultz, Tanja and Westphal, Martin and Waibel, Alex	https://www.ri.cmu.edu/pub_files/pub1/soltau_hagen_1998_2/soltau_hagen_1998_2.pdf	No	MGR	No	Inhouse	No	DNN	No	No	No	No	10x5 cepstral coefficients	2D	No	No	No	No	No	
@@ -37,7 +38,7 @@ Year	Entrytype	Title	Author	Link	Code	Task	Reproducible	Dataset	Framework	Archit
 2014	inproceedings	Boundary detection in music structure analysis using convolutional neural networks	Ullrich, Karen and Schlüter, Jan and Grill, Thomas	https://dav.grrrr.org/public/pub/ullrich_schlueter_grill-2014-ismir.pdf		Boundary detection		[SALAMI](http://ddmal.music.mcgill.ca/research/salami/annotations)							Mel-spectrogram			Cross-entropy				
 2014	inproceedings	Improving content-based and hybrid music recommendation using deep learning	Wang, Xinxi and Wang, Ye	http://www.smcnus.org/wp-content/uploads/2014/08/reco_MM14.pdf		Recommendation		[Echo Nest Taste Profile Subset](https://labrosa.ee.columbia.edu/millionsong/tasteprofile) & [7digital](https://7digital.com)	Theano	DBN			No								15 nodes of 2 Tesla M2090	
 2014	inproceedings	A deep representation for invariance and music classification	Zhang, Chiyuan and Evangelopoulos, Georgios and Voinea, Stephen and Rosasco, Lorenzo and Poggio, Tomaso	http://www.mirlab.org/conference_papers/International_Conference/ICASSP%202014/papers/p7034-zhang.pdf		MGR		[GTzan](http://marsyas.info/downloads/datasets.html)		CNN												
-2015	inproceedings	Auralisation of deep convolutional neural networks: Listening to learned features	Choi, Keunwoo and Fazekas, György and Sandler, Mark Brian and Kim, Jeonghee	http://ismir2015.uma.es/LBD/LBD24.pdf		MGR		Inhouse							STFT							
+2015	inproceedings	Auralisation of deep convolutional neural networks: Listening to learned features	Choi, Keunwoo and Fazekas, György and Sandler, Mark Brian and Kim, Jeonghee	http://ismir2015.uma.es/LBD/LBD24.pdf	https://github.com/keunwoochoi/Auralisation	MGR		Inhouse							STFT							
 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																
 2015	inproceedings	Music boundary detection using neural networks on spectrograms and self-similarity lag matrices	Grill, Thomas and Schlüter, Jan	http://www.ofai.at/~jan.schlueter/pubs/2015_eusipco.pdf		Boundary detection		[SALAMI](http://ddmal.music.mcgill.ca/research/salami/annotations)							STFT							
 2015	inproceedings	Classification of spatial audio location and content using convolutional neural networks	Hirvonen, Toni	https://www.researchgate.net/profile/Toni_Hirvonen/publication/276061831_Classification_of_Spatial_Audio_Location_and_Content_Using_Convolutional_Neural_Networks/links/5550665908ae12808b37fe5a/Classification-of-Spatial-Audio-Location-and-Content-Using-Convolutional-Neural-Networks.pdf