README.md

Healing Music Classifier

This project uses machine learning to classify whether a piece of music has healing properties or not. It analyzes various audio features including MFCC, spectral characteristics, rhythm, and harmonic content to make predictions.

Features

• Audio feature extraction using librosa
• Machine learning classification using Random Forest
• Web interface for easy music upload and analysis
• Visual results with healing probability score
• Cross-validation for model evaluation

Installation

1. Clone this repository
2. Install dependencies:

pip install -r requirements.txt

Usage

1. First, train the model:

python train_model.py

2. Run the web application:

streamlit run app.py

3. Open your browser and upload a music file to analyze

Project Structure

• train_model.py: Feature extraction and model training
• predict.py: Prediction functionality
• app.py: Streamlit web interface
• requirements.txt: Project dependencies
• model.joblib: Trained model (generated after training)
• scaler.joblib: Feature scaler (generated after training)

Technical Details

The classifier uses the following features:

• Mel-frequency cepstral coefficients (MFCC)
• Spectral centroid
• Spectral rolloff
• Zero crossing rate
• Chroma features
• Tempo
• RMS energy

Deployment

Local Deployment

1. Clone this repository
2. Install dependencies:

pip install -r requirements.txt

3. Run the web application:

streamlit run app.py

Cloud Deployment (Streamlit Cloud)

1. Fork this repository to your GitHub account
2. Visit Streamlit Cloud
3. Sign in with your GitHub account
4. Click "New app" and select this repository
5. Select the main branch and the app.py file
6. Click "Deploy"

Note: Make sure to include some sample music files in the healing_music and non_healing_music folders for training the model.

License

MIT License

Contributing

Feel free to open issues and pull requests!