/MusicGenreMetaClassifier

Source code for proceedings "Comparing Meta-Classifiers for Automatic Music Genre Classification".

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

Comparing Meta-Classifiers for Automatic Music Genre Classification

Source code from proceding Comparing Meta-Classifiers for Automatic Music Genre Classification published at 17th Brazilian Symposium on Computer Music (SBCM).

Getting Started

These instructions will get you a copy of the project up and running on your local machine for experiment reproducibility.

Prerequisites

  • Python 3.x
  • Libraries listed here

Installing

A step by step series of examples that tell you how to get everything set up to run the experiment.

  1. The first step is clone this repository.
$ git clone https://github.com/vitorys/MusicGenreMetaClassifier.git
  1. (Optional) Create a virtual enviroment and activate it.
$ virtualenv venv && activate venv/bin/activate
  1. Install the requirements.
$ pip install -r requirements.txt

Running experiments

To run the Neural Network experiments:

  1. Navigate to NeuralNetwork folder.
  2. Execute the python file main.py follow by some dataset. For example:
python main.py data/gtzan-ds_rp-feats_frames
  1. The result will be stored at output/ folder.

To run the Hidden Markov experiments:

  1. Navigate to HMM folder.
  2. Execute the python file main.py follow by --input and some dataset. For example:
python classifier.py --input data/gtzan-ds_rp-feats_frames
  1. The result will be stored at output/ folder.

Authors

  • Vítor Yudi Shinohara - State University of Campinas
  • Juliano Henrique Foleiss - Federal University of Technology - Paraná
  • Tiago Fernandes Tavares - State University of Campinas