ASMD: Audio-Score Meta Dataset

Table of Contents

This paper describes an open-source Python framework for handling datasets for music processing tasks, built with the aim of improving the reproducibility of research projects in music computing and assessing the generalization abilities of machine learning models. The framework enables the automatic download and installation of several commonly used datasets for multimodal music processing. Specifically, we provide a Python API to access the datasets through Boolean set operations based on particular attributes, such as intersections and unions of composers, instruments, and so on. The framework is designed to ease the inclusion of new datasets and the respective ground-truth annotations so that one can build, convert, and extend one’s own collection as well as distribute it by means of a compliant format to take advantage of the API. All code and ground-truth are released under suitable open licenses.

For a gentle introduction, see our paper [1]

TODO

  1. add automatic matching of songs among multiple datasets based on metadata (and maybe audio ID?)
  2. change the filter function for each level of filtering which takes keyword and value and filter that keyword at that level
  3. describe datasets provided by default
  4. generic description of the framework
  5. improve “adding_datasets” with a full example
  6. add section “examples”
  7. move wget to curl
  8. support Windows systems

Cite us

[1] Simonetta, Federico ; Ntalampiras, Stavros ; Avanzini, Federico: ASMD: an automatic framework for compiling multimodal datasets. In: Proceedings of the 17th Sound and Music Computing Conference. Torino, 2020 arXiv:2003.01958

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Federico Simonetta