🎵 Music Genre Classifier and Equalizer

📋 Description

This project analyzes the audio being played on your computer, converts it into a MEL spectrogram, and uses a deep learning model to classify the music genre. Based on the detected genre, it sends a command to JamesDSP to adjust the music equalization according to the genre-specific profile.

If you want to know more about the reason for certain selected parameters of the Mel programs, please visit the following document.

🧩 Features

Audio Capture: Monitors and captures the audio being played on your computer.
MEL Spectrogram Conversion: Converts the captured audio into a MEL spectrogram.
Music Genre Classification: Utilizes a deep learning model to classify the genre of the audio.
Automatic Equalization Adjustment: Sends commands to JamesDSP to adjust the equalization based on the detected music genre.

🛠️ Installation

Clone the repository:

git clone https://github.com/yourusername/music-genre-classifier.git
cd music-genre-classifier

Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install the required packages:
```
pip install -r requirements.txt
```

🕹️ Usage

Start the main script to begin monitoring audio and adjusting equalization:
```
python main.py
```
The system will capture the audio, generate the MEL spectrogram, classify the music genre, and adjust the equalization in real-time.

🎼 Project Structure

data/
- balance_dataset.py: Balances the dataset for training.
- command_equalization.py: Sends commands to JamesDSP for equalization adjustments.
- data_acquisition.py: Captures audio being played on the computer.
- music_2_spectrogram.py: Converts audio to MEL spectrogram.
model/
- schedulers/: Contains learning rate schedulers.
- architecture.py: Defines the architecture of the deep learning model.
- inference.py: Handles inference for genre classification.
main.py: Main script to run the complete audio analysis and equalization adjustment flow.
training.py: Script for training the deep learning model.

🫂 Contributing

Contributions are welcome. If you would like to contribute, please follow these steps:

Fork the project.
Create a new branch (git checkout -b feature/new_feature).
Make your changes and commit them (git commit -am 'Add new feature').
Push to the branch (git push origin feature/new_feature).
Open a Pull Request.

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

Thank you for using our automatic music genre classification and equalization system!

danibcorr/MusicGenreClassifier