Metadata-Version: 2.1
Name: acoss
Version: 0.0.1
Summary: Audio Cover Song Suite (acoss): A benchmarking suite for cover song identification tasks
Home-page: https://github.com/furkanyesiler/acoss
Author: Albin Correya, Furkan Yesiler, Chris Traile, Philip Tovstogan, and Diego Silva
Author-email: albin.correya@upf.edu
License: AGPL3.0
Project-URL: Source, https://github.com/furkanyesiler/acoss
Project-URL: Tracker, https://github.com/furkanyesiler/acoss/issues
Keywords: audio music dsp musicinformationretireval coversongidentification
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: docs
Requires-Dist: madmom
Requires-Dist: numpy (>=1.16.5)
Requires-Dist: pandas
Requires-Dist: scipy (==1.2.1)
Requires-Dist: scikit-learn (==0.19.2)
Requires-Dist: deepdish
Requires-Dist: essentia
Provides-Extra: docs
Provides-Extra: tests

# acoss: Audio Cover Song Suite
[![Build Status](https://travis-ci.org/furkanyesiler/acoss.svg?branch=packaging)](https://travis-ci.org/furkanyesiler/acoss)
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)
[![License: AGPL v3](https://img.shields.io/badge/License-AGPL%20v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)

[acoss: Audio Cover Song Suite]() is a feature extraction and benchmarking frameworks for the 
cover song identification tasks. This tool has been developed along with the new DA-TACOS dataset. 

## Setup & Installation

We recommend you to install the python package from source. 

#### Install from source (recommended)

- Clone or download the repo.
- Install `acoss` package by using the following command inside the directory.
```bash
python3 setup.py install
```

#### Install using pip

```bash
pip3 install acoss
```

> NOTE: While using pip install, you might need to have a local installation of [librosa](https://librosa.github.io/) 
python library.

## How to cite

Please site our paper if you use this tool in your resarch.

> Furkan Yesiler, Chris Tralie, Albin Correya, Diego F. Silva, Philip Tovstogan, Emilia Gómez, and Xavier Serra. Da-TACOS: A Dataset for Cover Song Identification and Understanding. In 20th International Society for Music Information Retrieval Conference (ISMIR 2019), Delft, The Netherlands, 2019.


## How to contribute?

* Fork the repo!
* Create your feature branch: git checkout -b my-new-feature
* Please read the [documentation]() for adding your new audio feature or cover identification algorithm to acoss.
* Commit your changes: git commit -am 'Add some feature'
* Push to the branch: git push origin my-new-feature
* Submit a pull request


## Acknowledgements

MIP-Frontiers, TROMPA



