Metadata-Version: 2.1
Name: barmuscomp
Version: 0.1.1
Summary: Package for barwise compression applied on musical segmentation.
Home-page: https://gitlab.inria.fr/amarmore/barmuscomp
Author: Marmoret Axel
Author-email: axel.marmoret@irisa.fr
License: BSD
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.8.*
Description-Content-Type: text/markdown
License-File: LICENSE.md
License-File: AUTHORS

# BarMusComp: Encoding songs with linear and nonlinear compression methods to reveal structure #

Hello, and welcome on this repository!

This project aims at compressing all bars in a song, and studies the compressed representations of every bar to infer its structure. It is related to my PhD thesis [1].

This repository contains code for the NTD, PCA, NMF, and Autoencoders (developed in PyTorch), as presented in [2].

This project is an extension of the toolbox as_seg [3], which computes the segmentation of an autosimilarity matrix.

It can be installed with pip using `pip install barmuscomp`.

This is a first release, and may contain bug. Comments are welcomed!

## 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.

## Tutorial Notebook ##

3 tutorial notebooks are available in the folder "Notebooks", and present the different compression methods on the song 'Come Together'.

## How to cite ##

You should cite the package `BarMusComp`, available on HAL (https://hal.archives-ouvertes.fr/hal-03782914).

Here are two styles of citations:

As a bibtex format, this should be cited as: @softwareversion{marmoret2022barmuscomp, title={BarMusComp: module for computing barwise compressed representations of music}, author={Marmoret, Axel and Cohen, J{\'e}r{\'e}my and Bimbot, Fr{\'e}d{\'e}ric}, URL={https://gitlab.inria.fr/amarmore/barmuscomp}, 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, BarMusComp: module for computing barwise compressed representations of music, 2022, url: https://gitlab.inria.fr/amarmore/barmuscomp.

## 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 FrÃ©dÃ©ric Bimbot (<bimbot@irisa.fr>).

## References ##
[1] A. Marmoret, "Unsupervised Machine Learning Paradigms for the Representation of Music Similarity and Structure", Ph.D. dissertation, UniversitÃ© de Rennes 1, 2022.
(not uploaded yet but will be soon! You should check the website hal.archives-ouvertes.fr/ in case this README is not updated with the reference.)

[2] A. Marmoret, J.E. Cohen, and F. Bimbot, "Barwise Compression Schemes for Audio-Based Music Structure Analysis"", in: 19th Sound and Music Computing Conference, SMC 2022, Sound and music Computing network, 2022.

[3] 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.
