Sound Classification App

A Flask web application that classifies audio sounds using deep learning. Upload WAV files and get instant predictions of the sound type.

Features

• 🎵 Upload WAV audio files
• 🤖 AI-powered sound classification
• 🎧 Audio playback functionality
• 📱 Responsive modern UI
• ⚡ Real-time predictions

Screenshots

Main Interface

Clean and modern upload interface with drag-and-drop functionality

Prediction Results

Example of sound classification results showing prediction results

Local Development

Prerequisites

• Python 3.10+
• pip

Installation

1. Clone the repository:

git clone <https://github.com/rbpata/Sound-Identification>
cd Sound-Classification

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

python app.py

4. Open your browser and go to http://localhost:5000

Project Structure

Sound-Classification/
├── app.py                 # Main Flask application
├── requirements.txt       # Python dependencies
├── render.yaml           # Render deployment configuration
├── classes.npy           # Label encoder classes
├── saved_models/         # Trained model files
│   └── audio_classification.keras
├── static/               # Static files (CSS)
│   └── style.css
├── templates/            # HTML templates
│   └── index.html
├── screenshots/          # App screenshots
│   └── README.md
└── uploads/             # Uploaded audio files
    └── .gitkeep

Deployment

This app is configured for deployment on Render:

1. Push your code to GitHub
2. Connect your repository to Render
3. Render will automatically detect the render.yaml configuration
4. The app will be deployed with the following settings:
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app:app
   • Environment: Python 3.10.12

Model Information

The app uses a pre-trained deep learning model for audio sound classification. The model was trained on the UrbanSound8K dataset and can classify sounds into various categories like:

• Air conditioner
• Car horn
• Children playing
• Dog bark
• Drilling
• Engine idling
• Gun shot
• Jackhammer
• Siren
• Street music

Technologies Used

• Backend: Flask, Python
• Machine Learning: TensorFlow, scikit-learn, librosa
• Frontend: HTML5, CSS3, JavaScript
• Deployment: Render, Gunicorn

License

This project is open source and available under the MIT License.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request