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Audio Cover Song Suite (acoss): A benchmarking suite for cover song identification tasks

Project description

acoss: Audio Cover Song Suite

Build Status Python 3.6 License: AGPL v3

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 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.
python3 setup.py install

Install using pip

pip3 install acoss

NOTE: While using pip install, you might need to have a local installation of librosa 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

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Files for acoss, version 0.0.1
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Filename, size acoss-0.0.1-cp36-cp36m-manylinux1_x86_64.whl (111.7 kB) File type Wheel Python version cp36 Upload date Hashes View hashes

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