TensorFlow code to perform end-to-end Optical Music Recognition on monophonic scores through Convolutional Recurrent Neural Networks and CTC-based training.
This repository was used for the experiments reported in the paper:
End-to-End Neural Optical Music Recognition of Monophonic Scores
@Article{Calvo-Zaragoza2018,
AUTHOR = {Calvo-Zaragoza, Jorge and Rizo, David},
TITLE = {End-to-End Neural Optical Music Recognition of Monophonic Scores},
JOURNAL = {Applied Sciences},
VOLUME = {8},
YEAR = {2018},
NUMBER = {4},
ARTICLE NUMBER = {606},
URL = {http://www.mdpi.com/2076-3417/8/4/606},
ISSN = {2076-3417},
DOI = {10.3390/app8040606}
}
This repository is intended for the Printed Images of Music Staves (PrIMuS) dataset.
PrIMuS can be donwloaded from https://grfia.dlsi.ua.es/primus/
Assuming that PrIMuS dataset has been downloaded, and all its samples has been placed in the same folder, the training of the models can be done with ctc_training.py. It is necessary to build a list of training samples and the set of symbols (vocabulary). Examples of these files are given in Datafolder.
python ctc_training.py -semantic -corpus <path_to_PrIMuS> -set Data/train.txt -vocabulary Data/vocabulary_semantic.txt -save_model ./trained_semantic_model
python ctc_training.py -corpus <path_to_PrIMuS> -set Data/train.txt -vocabulary Data/vocabulary_agnostic.txt -save_model ./trained_agnostic_model
For running inference over an input image, ctc_predict.py can be used, along with an input image, a trained model, and the corresponding vocabulary file.
The repository is not provided with trained models but these can be download from:
These models were the result of the traning process for one of the folds of the 10-fold cross-validation considered in the paper.
Let's see an example for the sample from PrIMuS provided in Data/Example:
This sample belongs to the test set of the aforementioned fold, so it was not seen by the networks during their training stage.
Running
python ctc_predict.py -image Data/Example/000051652-1_2_1.png -model Models/semantic_model.meta -vocabulary Data/vocabulary_semantic.txt
should get the following prediction
clef-C1 keySignature-EbM timeSignature-2/4 multirest-23 barline rest-quarter rest-eighth note-Bb4_eighth barline note-Bb4_quarter. note-G4_eighth barline note-Eb5_quarter. note-D5_eighth barline note-C5_eighth note-C5_eighth rest-quarter barline
The ground-truth of this example is given in Data/Example/000051652-1_2_1.semantic:
clef-C1 keySignature-EbM timeSignature-2/4 multirest-23 barline rest-quarter rest-eighth note-Bb4_eighth barline note-Bb4_quarter. note-G4_eighth barline note-Eb5_quarter. note-D5_eighth barline note-C5_eighth note-C5_eighth rest-quarter barline
It can be observed that the staff section is perfectly recognized by the model.
Running
python ctc_predict.py -image Data/Example/000051652-1_2_1.png -model Models/agnostic_model.meta -vocabulary Data/vocabulary_agnostic.txt
should get the following prediction
clef.C-L1 accidental.flat-L4 accidental.flat-L2 accidental.flat-S3 digit.2-L4 digit.4-L2 digit.2-S5 digit.3-S5 multirest-L3 barline-L1 rest.quarter-L3 rest.eighth-L3 note.eighth-L4 barline-L1 note.quarter-L4 dot-S4 note.eighth-L3 barline-L1 note.quarter-S5 dot-S5 note.eighth-L5 barline-L1 note.eighth-S4 note.eighth-S4 rest.quarter-L3
The ground-truth of this example is given in Data/Example/000051652-1_2_1.agnostic:
clef.C-L1 accidental.flat-L4 accidental.flat-L2 accidental.flat-S3 digit.2-L4 digit.4-L2 digit.2-S5 digit.3-S5 multirest-L3 barline-L1 rest.quarter-L3 rest.eighth-L3 note.eighth-L4 barline-L1 note.quarter-L4 dot-S4 note.eighth-L3 barline-L1 note.quarter-S5 dot-S5 note.eighth-L5 barline-L1 note.eighth-S4 note.eighth-S4 rest.quarter-L3 barline-L1
As discussed in the paper, this representation often misses the last barline.
- Jorge Calvo Zaragoza ([email protected])