asmd 0.2.2.post2

Audio-Score Meta-Dataset

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 audioscoredataset as asd

Other examples in the paper!

Changelog

Version 0.2.2-2

1. Fixed major bug in install script

2. Fixed bug in conversion tool

3. Removed TRIOS dataset because no longer available

4. Updated ground_truth

Version 0.2.2

1. Improved parallel function

2. Improved documentation

3. Various fixings in get_pedaling

Version 0.2.1

1. Added nframes utility to compute the number of frames in a given time lapse

2. Added group attribute to each track to create splits in a dataset (supported in only Maestro for now)

3. Changed .pyx to .py with cython in pure-python mode

Version 0.2

1. Added parallel utility to run code in parallel over a while dataset

2. Added get_pianoroll utility to get score as pianoroll

3. Added sustain, sostenuto, and soft to model pedaling information

4. Added utilities frame2time and time2frame to ease the development

5. Added get_audio_data to get data about audio without loading it

6. Added get_score_duration to get the full duration of a score without loading it

7. Added another name for the API: from asmd import asmd

8. Deprecated from asmd import audioscoredataset

9. Changed the generate_ground_truth command line options

10. Easier to generate misaligned data

11. Improved documentation

Roadmap

1. Added torch.DatasetDump for preprocessing datasets and use them in pytorch

2. Add new modalities (video, images)

3. Improve the artificial misalignment

4. Add datasets for the artificial misalignment (e.g. ASAP, Giant-Midi Piano)

5. Add other datasets

6. Refactoring of the API (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

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Federico Simonetta

1. https://federicosimonetta.eu.org

2. https://lim.di.unimi.it