Audio-Score Meta-Dataset
ASMD is a framework for installing, using and creating music multimodal datasets including (for now) audio and scores.
This is the repository for paper [1]
Read more in the docs.
To install: pip install asmd
To install datasets: python -m asmd.install
To import API: from asmd import asmd
Other examples in the paper!
Changelog
Version 0.5
Improved inference of misalignments
Improved the reproducibility of artificial data
Improved the documentation
Added ASAP group in Maestro: broken backward compatibility
Added score for the original score
Added midi note matching based on EITA method
Added missing and extra notes
General refactoring
Added functions for set operations among datasets (union, intersections, complement and subsampling)
Various fixes
Version 0.4
# Skipped
Version 0.3
Fixed MIDI values ([0, 128) for control changes and pitches)
Fixed metadata error while reading audio files
Fixed pedaling for tracks that have no pedaling
Fixed group selection
Added get_songs
Improved initialization of Dataset objects
Improved documentation
Version 0.2.2-2
Fixed major bug in install script
Fixed bug in conversion tool
Removed TRIOS dataset because no longer available
Updated ground_truth
Version 0.2.2
Improved parallel function
Improved documentation
Various fixings in get_pedaling
Version 0.2.1
Added nframes utility to compute the number of frames in a given time lapse
Added group attribute to each track to create splits in a dataset (supported in only Maestro for now)
Changed .pyx to .py with cython in pure-python mode
Version 0.2
Added parallel utility to run code in parallel over a while dataset
Added get_pianoroll utility to get score as pianoroll
Added sustain, sostenuto, and soft to model pedaling information
Added utilities frame2time and time2frame to ease the development
Added get_audio_data to get data about audio without loading it
Added get_score_duration to get the full duration of a score without loading it
Added another name for the API: from asmd import asmd
Deprecated from asmd import audioscoredataset
Changed the generate_ground_truth command line options
Easier to generate misaligned data
Improved documentation
Roadmap
Add matching of same music piece among different datasets
Added torch.DatasetDump for preprocessing datasets and use them in pytorch
Add new modalities (video, images)
Add other datasets
Refactoring of the filter function (it’s a bit long now…)
Cite us
[1] Simonetta, Federico ; Ntalampiras, Stavros ; Avanzini, Federico: ASMD: an automatic framework for compiling multimodal datasets with audio and scores. In: Proceedings of the 17th Sound and Music Computing Conference. Torino, 2020 arXiv:2003.01958
—
Federico Simonetta
1 maintainer
Metadata
1 maintainer
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