Metadata-Version: 2.1
Name: barmuscomp
Version: 0.1.0
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

# Barwise Music Compression: 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.

This repository contains code for the PCA, NMF, and Autoencoders, developed in PyTorch, and segmentation methods based on autosimilarity segmentation, as presented in [1].

It will soon de uploaded on PyPi, for pip install In the meantime, you can download the source files.

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.

## Example Notebook ##

An example notebook is available in the folder "Notebooks", and presents the song 'Come Together' with different features.

## Credits ##

Code was created by Axel Marmoret (<axel.marmoret@irisa.fr>), and strongly supported by Jeremy E. Cohen (<jeremy.cohen@irisa.fr>).

The technique in itself was also developed by FrÃ©dÃ©ric Bimbot (<bimbot@irisa.fr>).

## References ##
[1] Marmoret, A., Cohen, J., Bertin, N., & Bimbot, F. (2020, October). Uncovering Audio Patterns in Music with Nonnegative Tucker Decomposition for Structural Segmentation. In ISMIR 2020-21st International Society for Music Information Retrieval.
