Parangonar is a Python package for note alignment of symbolic music. Parangonar uses Partitura as file I/O utility. Note alignments produced py Parangonar can be visualized using the web tool Parangonda
The easiest way to install the package is via pip
from the PyPI (Python
Package Index):
pip install parangonar
This will install the latest release of the package and will install all dependencies automatically.
The following code snippets load the contents of a a previously aligned performance and score alignment file (encoded in the match file format).
A new alignment is computed using different note matchers and the predicted alignment are compared to the ground truth:
For an interactive version of these snippets, check the getting_started.ipynb
notebook.
import parangonar as pa
import partitura as pt
perf_match, groundtruth_alignment, score_match = pt.load_match(
filename= pa.EXAMPLE,
create_score=True
)
# compute note arrays from the loaded score and performance
pna_match = perf_match[0].note_array()
sna_match = score_match[0].note_array()
# match the notes in the note arrays --------------------- DualDTWNoteMatcher
sdm = pa.AutomaticNoteMatcher()
pred_alignment = sdm(sna_match,
pna_match,
verbose_time=True)
# compute f-score and print the results
print('------------------')
types = ['match','insertion', 'deletion']
for alignment_type in types:
precision, recall, f_score = pa.fscore_alignments(pred_alignment,
groundtruth_alignment,
alignment_type)
print('Evaluate ',alignment_type)
print('Precision: ',format(precision, '.3f'),
'Recall ',format(recall, '.3f'),
'F-Score ',format(f_score, '.3f'))
print('------------------')
# this matcher requires grace note info
sna_match = score_match[0].note_array(include_grace_notes=True)
# match the notes in the note arrays --------------------- DualDTWNoteMatcher
sdm = pa.DualDTWNoteMatcher()
pred_alignment = sdm(sna_match,
pna_match,
process_ornaments=False,
score_part=score_match[0]) # if a score part is passed, ornaments can be handled seperately
# compute f-score and print the results
print('------------------')
types = ['match','insertion', 'deletion']
for alignment_type in types:
precision, recall, f_score = pa.fscore_alignments(pred_alignment,
groundtruth_alignment,
alignment_type)
print('Evaluate ',alignment_type)
print('Precision: ',format(precision, '.3f'),
'Recall ',format(recall, '.3f'),
'F-Score ',format(f_score, '.3f'))
print('------------------')
import parangonar as pa
import partitura as pt
score = pt.load_score(filename= 'path/to/score_file')
performance = pt.load_performance_midi(filename= 'path/to/midi_file')
# compute note arrays from the loaded score and performance
pna = performance.note_array()
sna = score.note_array()
# match the notes in the note arrays
sdm = pa.AutomaticNoteMatcher()
pred_alignment = sdm(sna, pna)
import parangonar as pa
import partitura as pt
perf_match, groundtruth_alignment, score_match = pt.load_match(
filename= pa.EXAMPLE,
create_score=True
)
# compute note arrays from the loaded score and performance
pna_match = perf_match.note_array()
sna_match = score_match.note_array()
# compute synthetic anchor points every 4 beats
nodes = pa.match.node_array(score_match[0],
perf_match[0],
groundtruth_alignment,
node_interval=4)
# match the notes in the note arrays
apdm = pa.AnchorPointNoteMatcher()
pred_alignment = apdm(sna_match,
pna_match,
nodes)
# compute f-score and print the results
print('------------------')
types = ['match','insertion', 'deletion']
for alignment_type in types:
precision, recall, f_score = pa.fscore_alignments(pred_alignment,
groundtruth_alignment,
alignment_type)
print('Evaluate ',alignment_type)
print('Precision: ',format(precision, '.3f'),
'Recall ',format(recall, '.3f'),
'F-Score ',format(f_score, '.3f'))
print('------------------')
import parangonar as pa
import partitura as pt
perf_match, groundtruth_alignment, score_match = pt.load_match(
filename= pa.EXAMPLE,
create_score=True
)
# compute note arrays from the loaded score and performance
pna_match = perf_match[0].note_array()
# this matcher requires grace note info
sna_match = score_match[0].note_array(include_grace_notes=True)
# set up the matcher using the score information: OnlineTransformerMatcher / OnlinePureTransformerMatcher
matcher = pa.OnlinePureTransformerMatcher(sna_match)
# the "offline" method loops over all notes in the performance and calls the "online" method for each one.
pred_alignment = matcher.offline(pna_match)
# compute f-score and print the results
print('------------------')
types = ['match','insertion', 'deletion']
for alignment_type in types:
precision, recall, f_score = pa.fscore_alignments(pred_alignment,
groundtruth_alignment,
alignment_type)
print('Evaluate ',alignment_type)
print('Precision: ',format(precision, '.3f'),
'Recall ',format(recall, '.3f'),
'F-Score ',format(f_score, '.3f'))
print('------------------')
import parangonar as pa
import partitura as pt
perf_match, alignment, score_match = pt.load_match(
filename= pa.EXAMPLE,
create_score=True
)
pna_match = perf_match.note_array()
sna_match = score_match.note_array()
# show or save plot of note alignment
pa.plot_alignment(pna_match,
sna_match,
alignment,s
save_file = False)
# or plot the performance and score as piano rolls given a reference:
# we can encode errors if given ground truth
# Blue lines indicate correct matches, red lines incorrect ones.
pa.plot_alignment_comparison(pna_match, sna_match,
pred_alignment, groundtruth_alignment)
Most I/O functions are handled by partitura. For Parangonada:
- pt.io.importparangonada.load_parangonada_alignment
- pt.io.importparangonada.load_parangonada_csv
- pt.io.exportparangonada.save_parangonada_alignment
- pt.io.exportparangonada.save_parangonada_csv
- pt.io.importparangonada.load_alignment_from_ASAP
- pt.io.exportparangonada.save_alignment_for_ASAP
For match files
- pt.io.importmatch.load_match
- pt.io.exportmatch.save_match
and a basic interface for saving parangonada-ready csv files is also available:
import partitura as pt
import parangonar as pa
# export a note alignment for visualization with parangonada:
# https://sildater.github.io/parangonada/
pa.match.save_parangonada_csv(alignment,
performance_data,
score_data,
outdir="path/to/dir")
# import a corrected note alignment from parangonada:
# https://sildater.github.io/parangonada/
alignment = pt.io.importparangonada.load_parangonada_alignment(filename= 'path/to/note_alignment.csv')
# load note alignments of the asap dataset:
# https://github.com/CPJKU/asap-dataset/tree/note_alignments
alignment = pt.io.importparangonada.load_alignment_from_ASAP(filename= 'path/to/note_alignment.tsv')
These note-aligned datasets are publically available:
Two publications are associated with models available in Parangonar.
The anchor point-enhanced AnchorPointNoteMatcher
and the automatic AutomaticNoteMatcher
are this described in:
@article{nasap-dataset,
title = {Automatic Note-Level Score-to-Performance Alignments in the ASAP Dataset},
author = {Peter, Silvan David and Cancino-Chacón, Carlos Eduardo and Foscarin, Francesco and McLeod, Andrew Philip and Henkel, Florian and Karystinaios, Emmanouil and Widmer, Gerhard},
doi = {10.5334/tismir.149},
journal = {Transactions of the International Society for Music Information Retrieval {(TISMIR)}},
year = {2023}
}
and the former is used in the creation of the note-aligned (n)ASAP Dataset.
The improved automatic DualDTWNoteMatcher
and the online / realtime OnlineTransformerMatcher
/ OnlinePureTransformerMatcher
are described in:
@inproceedings{peter-2023,
title={Online Symbolic Music Alignment with Offline Reinforcement Learning},
author={Peter, Silvan David},
booktitle={International Society for Music Information Retrieval Conference {(ISMIR)}},
year={2023}
}
This work is supported by the European Research Council (ERC) under the EU’s Horizon 2020 research & innovation programme, grant agreement No. 10101937 (”Wither Music?”).
The code in this package is licensed under the Apache 2.0 License. For details, please see the LICENSE file.