🎵 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

1. Clone the repository:

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

2. Create a virtual environment (optional but recommended):

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

3. Install the required packages:

   pip install -r requirements.txt

🕹️ Usage

1. Start the main script to begin monitoring audio and adjusting equalization:

   python main.py

2. 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:

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

📜 License

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

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