fix #17

README.md

@@ -195,7 +195,7 @@ However, these surveys do not cover music information retrieval tasks that are i
 | 2018 | [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) |
 | 2018 | [Music transformer: Generating music with long-term structure](https://arxiv.org/pdf/1809.04281.pdf) | No |
 | 2018 | [Music theory inspired policy gradient method for piano music transcription](https://nips2018creativity.github.io/doc/music_theory_inspired_policy_gradient.pdf) | No |
-| 2019 | [Enabling factorized piano music modeling and generation with the MAESTRO dataset](https://arxiv.org/abs/1810.12247) | No |
+| 2019 | [Enabling factorized piano music modeling and generation with the MAESTRO dataset](https://arxiv.org/abs/1810.12247) | [GitHub](https://github.com/magenta/magenta/tree/master/magenta/models/onsets_frames_transcription) |
 | 2019 | [Generating Long Sequences with Sparse Transformers](https://arxiv.org/pdf/1904.10509.pdf) | [GitHub](https://github.com/openai/sparse_attention) |
 
 [Go back to top](https://github.com/ybayle/awesome-deep-learning-music#deep-learning-for-music-dl4m-)
@@ -261,7 +261,7 @@ 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 44 articles (26%) provide their source code.
+- Only 45 articles (27%) 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-)

dl4m.bib

@@ -2166,7 +2166,7 @@ @inproceedings{Hawthorne2019
   author = {Hawthorne, Curtis and Stasyuk, Andriy and Roberts, Adam and Simon, Ian and Huang, Cheng-Zhi Anna and Dieleman, Sander and Elsen, Erich and Engel, Jesse and Eck, Douglas},
   batch = {No},
   booktitle = {ICLR},
-  code = {No},
+  code = {https://github.com/magenta/magenta/tree/master/magenta/models/onsets_frames_transcription},
   computationtime = {No},
   dataaugmentation = {pitch-shift {+-0.1 semitones} & compression {0 - 100} & EQ {32 - 4096} & Reverb & {0 - 70} & Pink-noise {0 - 0.04}},
   dataset = {[MAESTRO](https://magenta.tensorflow.org/datasets/maestro/)},

dl4m.tsv

@@ -162,5 +162,5 @@ Year	Entrytype	Title	Author	Link	Code	Task	Reproducible	Dataset	Framework	Archit
 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	
 2018	unpublished	Music transformer: Generating music with long-term structure	Huang, Cheng-Zhi Anna and Vaswani, Ashish and Uszkoreit, Jakob and Shazeer, Noam and Simon, Ian and Hawthorne, Curtis and Dai, Andrew M. and Hoffman, Matthew D. and Dinculescu, Monica and Eck, Douglas	https://arxiv.org/pdf/1809.04281.pdf		Polyphonic music sequence modelling	No	[J.S. Bach chorales dataset](https://github.com/czhuang/JSB-Chorales-dataset) & [Piano-e-Competition dataset (competition history)](http://www.piano-e-competition.com/)	No	Transformer & RNN & tensor2tensor	0.1	1	No	Time Stretches & pitch transcription	MIDI	1D	No	No	0.1	No	No	
 2018	inproceedings	Music theory inspired policy gradient method for piano music transcription	Li, Juncheng and Qu, Shuhui and Wang, Yun and Li, Xinjian and Das, Samarjit and Metze, Florian	https://nips2018creativity.github.io/doc/music_theory_inspired_policy_gradient.pdf	No	Transcription	No	[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/)		CNN & RNN	No	8	No	No	Log Mel-spectrogram with 48 bins per octave and 512 hop-size and 2018 window size and 16 kHz sample rate	2D	No	binary cross-entropy	0.0006	Adam	No	
-2019	inproceedings	Enabling factorized piano music modeling and generation with the MAESTRO dataset	Hawthorne, Curtis and Stasyuk, Andriy and Roberts, Adam and Simon, Ian and Huang, Cheng-Zhi Anna and Dieleman, Sander and Elsen, Erich and Engel, Jesse and Eck, Douglas	https://arxiv.org/abs/1810.12247	No	Transcription	No	[MAESTRO](https://magenta.tensorflow.org/datasets/maestro/)	No	No	No	No	No	pitch-shift {+-0.1 semitones} & compression {0 - 100} & EQ {32 - 4096} & Reverb & {0 - 70} & Pink-noise {0 - 0.04}	MIDI	1D	No	No	No	No	No	
+2019	inproceedings	Enabling factorized piano music modeling and generation with the MAESTRO dataset	Hawthorne, Curtis and Stasyuk, Andriy and Roberts, Adam and Simon, Ian and Huang, Cheng-Zhi Anna and Dieleman, Sander and Elsen, Erich and Engel, Jesse and Eck, Douglas	https://arxiv.org/abs/1810.12247	https://github.com/magenta/magenta/tree/master/magenta/models/onsets_frames_transcription	Transcription	No	[MAESTRO](https://magenta.tensorflow.org/datasets/maestro/)	No	No	No	No	No	pitch-shift {+-0.1 semitones} & compression {0 - 100} & EQ {32 - 4096} & Reverb & {0 - 70} & Pink-noise {0 - 0.04}	MIDI	1D	No	No	No	No	No	
 2019	unpublished	Generating Long Sequences with Sparse Transformers	Rewon Child and Scott Gray and Alec Radford and Ilya Sutskever	https://arxiv.org/pdf/1904.10509.pdf	https://github.com/openai/sparse_attention	Audio generation	No	[413 hours of recorded solo piano music](http://papers.nips.cc/paper/8023-the-challenge-of-realistic-music-generation-modelling-raw-audio-at-scale-supplemental.zip)	Tensorflow	Transformer	0.25	No	120	No	Raw Audio	1D	No	No	0.00035	Adam	8 NVIDIA Tesla V100