# Acknowledgments

## Authorship

`musif` is the result of interdisciplinary work within the ERC project [“DIDONE”](https://didone.eu). The following 
are the people involved in its creation.

### Conception
**Ana Llorens** is Assistant Professor of Music Theory at the Universidad Complutense de 
Madrid. She received her PhD from the University of Cambridge and is expert in the analysis
of large corpora of music, both scores and recordings. She is responsible for the conception,
design, and revision of musif.

**Álvaro Torrente**, PI of the Didone Project, is Professor of Musicology at the Universidad 
Complutense de Madrid and director of the Instituto Complutense de Ciencias Musicales. He
already applied corpus analysis in his doctoral dissertation (Cambridge, 1997), and his is the
original idea of designing a computational tool for extracting specific features from music 
scores that could be used for music analysis and AI tasks.

### Coding and documentation

**Martín Serrano**, programmer of the Didone project, was in charge of the coding from the 
initial to the last stages, as well as the implementation of unit tests.

**Federico Simonetta**, computer scientist, has extensively worked on Music Information 
Retrieval, obtaining his PhD at the Università degli studi di Milano. As a postdoc researcher 
withing the Didone project, he polished musif code in its final stage and prepared the 
documentation and website. He tested the library on multiple corpora.

**Daniel Ibáñez**, computer scientist, collaborated in the coding during the intermediate phase.

**Paula Muñoz-Lago**, computer scientist, contributed to the initial code and first stages of the 
tool.

**Carlos Vaquero Patricio**, received a PhD from the Institute for Logic, Language and Computation (Music Cognition Group) at the University of Amsterdam. Joined Didone as a postdoc researcher (Data Scientist) in 2023. Contributed to the final stages of musif, debugging, developing and making it accessible to the MIR community.


### Acknowledgments
`musif` is a result of the Didone Project, which has received funding from the European 
Research Council (ERC) under the European Union’s Horizon 2020 research and innovation
program, Grant agreement No. 788986. We also acknowledge financial support from Spain’s
Ministerio de Ciencia e Innovación ([IJC2020-\-043969-I/\-AEI/\-10.13039/\-501100011033](https://doi-org10.13039/\-501100011033))

**Eduardo García-Portugués** is Associate Professor of Statistics at the Universidad Carlos III 
de Madrid. He contributed to musif by defining several features within the rhythm module.

## How to Contribute
If you have any issues or want to contribute new features, tests, or documentation, contact 
the lead author on GitHub or [her email address](allorens@ucm.es). If you want to open an issue, please do so on our [GitHub repo](https://github.com/DIDONEproject/musif).

## How to Cite

When using it, please cite `musif` as Llorens, Ana; Simonetta, Federico; Serrano, Martín; Torrente, Álvaro. "musif: a Python package for symbolic music feature extraction", *Sound and Music Computing Conference 2023*, Stockholm, 12-17 June 2023 (to appear).

## Licensing
`musif` is Copyright © 2023, Ana Llorens, Álvaro Torrente, [ICCMU](https://iccmu.es). `musif` code (excluding 
content encoded in the corpus) is free and open-source software, licensed under the BSD 
License.