mariaalfaroc / smc-2022

Transfer learning between OMR and A2S models

Insights into transfer learning between image and audio music transcription

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About • How To Use • Citations • Acknowledgments • License

About

We consider a music transcription system, trained on either image (Optical Music Recognition, OMR) or audio (Automatic Music Transcription, AMT)¹ data, and adapt its performance to the unseen domain during the training phase using different transfer learning schemes.

How To Use

Dataset

We use the Camera-PrIMuS dataset.

The Camera-PrIMuS dataset contains 87 678² real-music incipits³, each represented by six files: (i) the Plaine and Easie code source, (ii) an image with the rendered score, (iii) a distorted image, (iv) the musical symbolic representation of the incipit both in Music Encoding Initiative format (MEI) and (v) in an on-purpose simplified encoding (semantic encoding), and (vi) a sequence containing the graphical symbols shown in the score with their position in the staff without any musical meaning (agnostic encoding).

To obtain the corresponding audio files, we must convert one of the provided representations to MIDI and then synthesize the MIDI data. We have opted to convert the semantic representation, as there is a publicly available semantic-to-MIDI converter. Once we have obtained the MIDI files, we render them using FluidSynth.

The specific steps to follow are:

1. Download the semantic-to-MIDI converter from here and place the omr-3.0-SNAPSHOT.jar file in the dataset folder.
2. Download a General MIDI SounFont (sf2). We recommend downloading the SGM-v2.01 soundfont as this code has been tested using this soundfont. Place the sf2 file in the dataset folder.

Experiments

We consider two scenarios:

• Scenario A. This scenario assesses the performance of the transcription models when transfer learning is both considered and ignored.
• Scenario B. This scenario studies the amount of data required in the target domain for an efficient transfer process that outperforms the base case of ignoring transfer learning.

To replicate our experiments, you will first need to meet certain requirements specified in the Dockerfile. Alternatively, you can set up a virtual environment if preferred. Once you have prepared your environment (either a Docker container or a virtual environment) and followed the steps in the dataset section, you are ready to begin. Follow this recipe to replicate our experiments:

Important note: To execute the following code, both Java and FluidSynth must be installed.

$ cd dataset
$ sh prepare_data.sh
$ cd ..
$ python main.py

Citations

@inproceedings{alfaro2022insights,
  title     = {{Insights into Transfer Learning between Image and Audio Music Transcription}},
  author    = {Alfaro-Contreras, Mar{\'\i}a and Valero-Mas, Jose J and I{\~n}esta, Jos{\'e} M and Calvo-Zaragoza, Jorge},
  booktitle = {{Proceedings of the 19th Sound and Music Computing Conference}},
  pages     = {295--301},
  year      = {2022},
  publisher = {Zenodo},
  month     = jun,
  address   = {Saint-Étienne, France},
  doi       = {10.5281/zenodo.6797870},
}

Acknowledgments

This work is part of the I+D+i PID2020-118447RA-I00 (MultiScore) project, funded by MCIN/AEI/10.13039/501100011033.

License

This work is under a MIT license.

Footnotes

1. It is important to clarify that the model we are referring to is actually an Audio-to-Score (A2S) model. At the time of conducting this research, we used the term AMT because the distinction between AMT and A2S did not exist in the literature. However, nowadays, there is a clear distinction between the two. AMT typically focuses on note-level transcription, encoding the acoustic piece in terms of onset, offset, pitch values, and the musical instrument of the estimated notes. In contrast, A2S aims to achieve a score-level codification. ↩

2. In this work, we consider 22 285 samples out of the total 87 678 that constitute the complete Camera-PrIMuS dataset. This selection resulted from a data curation process, primarily involving the removal of samples containing long multi-rests. These music events contribute minimally to the length of the score image but may span a large number of frames in the audio signal. ↩

3. An incipit is a sequence of notes, typically the first ones, used for identifying a melody or musical work. ↩