This repository provides the Matlab scripts useful to generate the analyses and the figures of the article:
- Thoret, E., Ystad, S., Kronland-Martinet, R. (2023) Hearing as adaptive cascaded envelope interpolation. Communications Biology
Each Matlab file corresponds to one figure of sub-figure from the paper. Each script should generate figures (.eps + .fig) and source data files (.csv) in folders './out/eps/', './out/fig/', and './out/csv/'. To avoid errors, please add to path, all the folders and subfolders before running the scripts.
A description of the output 'csv' file is provided in comments at the beginning of each script and also in 'csv_descriptions.txt'
These scripts are using libraries embedded in the subfoldeer ./lib/ in particular the Empirical Modes Decomposition library developed by Patrick Flandrin and Gabriel Rilling: https://perso.ens-lyon.fr/patrick.flandrin/emd.html
Please cite us and them if you use parts of these scripts :
- Flandrin, P., Rilling, G., & Goncalves, P. (2004). Empirical mode decomposition as a filter bank. IEEE signal processing letters, 11(2), 112-114.
- Rilling, G., & Flandrin, P. (2007). One or two frequencies? The empirical mode decomposition answers. IEEE transactions on signal processing, 56(1), 85-95.
Other functions that have been picked up on different repositories can be found here:
- 'btqn.m': Bradley-Terry algorithm http://personal.psu.edu/drh20/code/btmatlab/
- The Making Sense of sounds dataset can be accessed here: https://doi.org/10.17866/rd.salford.6901475.v4
- Basically, all the excerpts from the development set have been pasted in the same subfolder './cmos/'
- Please cite them if re-used: Harris, Lara; Bones, Oliver Charles (2018). Making Sense Of Sounds: Data for the machine learning challenge 2018. University of Salford. Dataset. https://doi.org/10.17866/rd.salford.6901475.v4
- Please email me if you need the exact folder I reorganized.
Please let me know for any bugs or questions: etiennethoret [at] gmail [dot] com