DeepMusicClassification

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DeepMusicClassification

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An implementation of a Convolutional Neural Network to Classify Music Genres

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<p align="center">
  <a href="https://www.python.org">
    <img src="assets/Python.png" alt="Python" width="400" height="175">
  </a>

<h3 align="center">DeepMusicClassification</h3>

<p align="center">
    An Implementation of a Convolutional Neural Network to Classify Music Genres
    <br />
    <a href="https://github.com/nlopez99/DeepMusicClassification/issues">Report Bug</a>
    ·
    <a href="https://github.com/nlopez99/DeepMusicClassification/issues">Request Feature</a>
  </p>
</p>

## Table of Contents

* [About the Project](#about-the-project)
  * [Built With](#built-with)
* [Getting Started](#getting-started)
  * [Prerequisites](#prerequisites)
  * [Installation](#installation)
* [Usage](#usage)
* [Roadmap](#roadmap)
* [Contributing](#contributing)
* [License](#license)
* [Contact](#contact)
* [Acknowledgements](#acknowledgements)

## About The Project
<p align="center">
  <a href="https://github.com/nlopez99/DeepMusicClassification">
    <img src="assets/topology.png" alt="Python" width="1200" height="1800">
  </a>
</p>

The goal of this project is to utilize the GTZAN dataset to train a convolutional neural network to classify melspectrograms into music genres. After training the model for some time, it reached 70.6% accuracy on the testing dataset.

About the model:
* The neural network topology includes 4 Convolutional layers
* The model includes batch normalization, L2 penalty for weight biases, and dropout layer to reduce overfitting
* Tensorboard callback for tracking model performance:smile:

### Built With
* [Python 3](https://www.python.org)
* [Keras](https://keras.io/)
* [TensorFlow](https://www.tensorflow.org/)

## Getting Started

To get DeepMusicClassification up and running locally, [Python3.5<](https://www.python.org/downloads/release/python-374/) must be installed and added to PATH.

### Prerequisites
Additional prerequisites to get DeepMusicClassification up and running.
* Update pip3 for most Linux ditros
```sh
sudo -H pip3 install --upgrade pip
```
* Update pip3 for Windows
```sh
python3 -m pip3 install --upgrade pip
```

### Installation

1. Clone the repo
```sh
git clone https://github.com/nlopez99/DeepMusicClassification.git
```
2. Pip3 install packages
```sh
pip3 install -r requirements.txt
```
3. Download [GTZAN](http://opihi.cs.uvic.ca/sound/genres.tar.gz) dataset and extract it to the datasets folder

## Usage

To train the model run:
```sh
python3 main.py -t train -d datasets/genres --epochs 20
```
To test the model against a song (WAV format) of your choosing:
```sh
python3 main.py -t test -s your_song.wav
```

## Roadmap

See the [open issues](https://github.com/nlopez99/DeepMusicClassification/issues) for a list of proposed features (and known issues).

## Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are **greatly appreciated**.

1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request

## License

Distributed under the MIT License. See `LICENSE` for more information.

## Contact

Nino Lopez - [@Nino_Lopez](https://twitter.com/Nino_Lopez) - antonino.lopez@spartans.ut.edu

Project Link: [https://github.com/nlopez99/DeepMusicClassification/](https://github.com/nlopez99/DeepMusicClassification/)

## Acknowledgements
* [Keras](https://keras.io/)
* [TensorFlow](https://www.tensorflow.org/)
* [Python3](https://python.org/)
* [Choose an Open Source License](https://choosealicense.com)
* [GitHub Pages](https://pages.github.com)

[issues-shield]: https://img.shields.io/github/issues/othneildrew/Best-README-Template.svg?style=flat-square
[issues-url]: https://github.com/nlopez99/DeepMusicClassification/issues
[license-shield]: https://img.shields.io/github/license/othneildrew/Best-README-Template.svg?style=flat-square
[license-url]: https://github.com/othneildrew/Best-README-Template/blob/master/LICENSE.txt
[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555
[linkedin-url]: https://www.linkedin.com/in/nino-lopez-tampa/
[product-screenshot]: assets/topology.png

DeepMusicClassification

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