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MAST Rhythmic Analysis

This repository contains a set of Jupyter notebooks that will guide us through some analysis over the MAST Rhythmic Dataset. A more detailed discussion regarding the results listed in the these notebooks can be checked in the paper

F. Falcao, B. Bozkurt, X. Serra, N. Andrade, O. Baysal, A Dataset of Rhythmic Patterns Reproductions and Baseline Automatic Assessment System, Proc. ISMIR (International Society for Music Information Retrieval Conference), 4-8 Nov., Delft, 2019.

  author={Falcao, F., Bozkurt, B., Serra, X., Andrade, N., Baysal, O.},
  title={A Dataset of Rhythmic Patterns Reproductions and Baseline Automatic Assessment System},
  booktitle={Proc. of the International Society for Music Information Retrieval Conference, ISMIR 2019}

The proposed analysis is fed by data from both the full MAST rhythm dataset and its re-annotated subset

To run the notebooks, you will need to install docker and run the Jupyter server available in the docker image.



In order to install docker you must follow the OS-specific instructions:


Mac OS


Docker Compose

See on how to install Docker Compose

Running Server

In the root of your project, run: docker-compose up

On Linux, run the following (this command ensures that any files you create are owned by your own user): JUPYTER_USER_ID=$(id -u) docker-compose up

The first time you run this command it will download the required docker images (about 2GB in size). If you have previously downloaded the images and would like to update them with the last version, run:

docker-compose pull

Then, you can access the notebooks from the browser and run them. All the notebooks contain their user guides, but you must to make sure to run them in a specific order, since one notebook might use outputs from a previous notebook. The correct sequence is: DownloadDataFromMASTDataset.ipynb -> PreProcessDataFromMASTDataset.ipynb -> BaselineRhythmicAssessmentSystem.ipynb

Please cite the publication mentioned above if you use this baseline system or the dataset in your work.

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