as_seg: module for computing and segmenting autosimilarity matrices.
Hello, and welcome on this repository!
This project aims at computing autosimilarity matrices, and segmenting them, which consists of the task of structural segmentation.
The current version contains the CBM algorithm [1], along with an implementation of Foote's novelty algorithm [2] based on the MSAF toolbox [3].
It can be installed using pip as pip install as-seg.
This is a first release, and may contain bug. Comments are welcomed!
Tutorial notebook
A tutorial notebook presenting the most important components of this toolbox is available in the folder "Notebooks".
Experimental notebook
Experimental notebooks are available in the folder "Notebooks". They present the code used to compute the main experiments of the paper, in order to improve the reproducibility. Please tell me if any problem would appear when trying to launch them.
Experimental Notebooks requires some pre-computed data to work, which can be found on zenodo: https://zenodo.org/records/10168387. DOI: 10.5281/zenodo.10168386.
Data
Should be obtained from Zenodo: https://zenodo.org/records/10168387. DOI: 10.5281/zenodo.10168386.
Software version
This code was developed with Python 3.8.5, and some external libraries detailed in dependencies.txt. They should be installed automatically if this project is downloaded using pip.
How to cite
You should cite the package as_seg, available on HAL (https://hal.archives-ouvertes.fr/hal-03797507).
Here are two styles of citations:
As a bibtex format, this should be cited as: @softwareversion{marmoret2022as_seg, title={as_seg: module for computing and segmenting autosimilarity matrices}, author={Marmoret, Axel and Cohen, J{'e}r{'e}my and Bimbot, Fr{'e}d{'e}ric}, LICENSE = {BSD 3-Clause ''New'' or ''Revised'' License}, year={2022}}
In the IEEE style, this should be cited as: A. Marmoret, J.E. Cohen, and F. Bimbot, "as_seg: module for computing and segmenting autosimilarity matrices," 2022, url: https://gitlab.inria.fr/amarmore/autosimilarity_segmentation.
Credits
Code was created by Axel Marmoret (axel.marmoret@gmail.com), and strongly supported by Jeremy E. Cohen (jeremy.cohen@cnrs.fr).
The technique in itself was also developed by Frédéric Bimbot (bimbot@irisa.fr).
References
[1] A. Marmoret, J.E. Cohen, F. Bimbot. Barwise Music Structure Analysis with the Correlation Block-Matching Segmentation Algorithm. Transactions of the International Society for Music Information Retrieval (TISMIR), 2023, 6 (1), pp.167-185. ⟨10.5334/tismir.167⟩. ⟨hal-04323556⟩, https://hal.science/hal-04323556.
[2] J. Foote, "Automatic audio segmentation using a measure of audio novelty," in: 2000 IEEE Int. Conf. Multimedia and Expo. ICME2000. Proc. Latest Advances in the Fast Changing World of Multimedia, vol. 1, IEEE, 2000, pp. 452–455.
[3] Nieto, O., Bello, J. P., Systematic Exploration Of Computational Music Structure Research. Proc. of the 17th International Society for Music Information Retrieval Conference (ISMIR). New York City, NY, USA, 2016.
[4] Böck, S., Korzeniowski, F., Schlüter, J., Krebs, F., & Widmer, G. (2016, October). Madmom: A new python audio and music signal processing library. In Proceedings of the 24th ACM international conference on Multimedia (pp. 1174-1178).