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AI-Powered Indian Classical Music Raga Detection Platform
A comprehensive platform for discovering, analyzing, and understanding Indian classical music through advanced AI technology.
- Getting Started - Your first steps with RagaSense
- User Guide - Complete user documentation
- Raga Database - Explore Indian classical ragas
- Troubleshooting - Common issues and solutions
- FAQ - Frequently asked questions
- Developer Setup - Development environment setup
- API Documentation - Complete API reference
- Architecture - System design and components
- Contributing - How to contribute to RagaSense
- Deployment - Production deployment guide
- ML System - Machine learning architecture
- Research Papers - Academic references
- Data Collection - Training data methodology
- Performance Metrics - Model evaluation
RagaSense is a revolutionary platform that combines the power of artificial intelligence with the rich tradition of Indian classical music. Our system can:
- Upload audio files or record live music
- Get instant raga identification with confidence scores
- Support for multiple audio formats (WAV, MP3, OGG, FLAC, M4A)
- Cross-platform compatibility (Web, iOS, Android)
- Machine learning models trained on extensive raga datasets
- Feature extraction using MFCCs, Chroma, and spectral analysis
- Real-time processing with sub-second response times
- Continuous learning and model improvement
- Detection history and statistics
- User preferences and favorites
- Performance analytics and insights
- Export capabilities for research
- Real-time database with Convex
- User authentication and profiles
- File management and storage
- Cross-platform synchronization
| Component | Technology | Purpose |
|---|---|---|
| Frontend | Lynx + ReactLynx | Cross-platform UI |
| Backend | FastAPI + Python | ML API & processing |
| Database | Convex | Real-time data & auth |
| ML | TensorFlow + Librosa | Raga detection |
| Build | Rspeedy | Cross-platform builds |
| Package Manager | Bun | Fast dependency management |
Our system currently supports detection of major ragas from both Hindustani and Carnatic traditions:
- Bhairav - Deep, meditative morning raga
- Ahir Bhairav - Peaceful dawn raga
- Todi - Complex morning raga
- Yaman - Beautiful evening raga
- Khamaj - Light classical raga
- Kafi - Popular evening raga
- Darbari Kanada - Deep evening raga
- Malkauns - Mysterious night raga
- Bhairavi - Traditional closing raga
| Metric | Value | Target |
|---|---|---|
| Detection Accuracy | 85%+ | 90%+ |
| Processing Time | <0.1s | <0.05s |
| Supported Formats | 5 | 8+ |
| Platform Support | 3 | 3 |
| Uptime | 99.9% | 99.99% |
-
Visit the App
https://adhit-r.github.io/RagaSense -
Upload Audio
- Drag and drop any audio file
- Or record live music directly
-
Get Results
- Instant raga identification
- Confidence scores and details
- Historical analysis
# Clone the repository
git clone https://github.com/adhit-r/RagaSense.git
cd raga_detector
# Start backend
python -m backend.main
# Start frontend
cd frontend
bun install
bun run dev- GitHub Issues - Report bugs and request features
- Discussions - Community discussions
- Wiki - This documentation
- Email Support - Direct support
- Contributing Guide - How to contribute
- Code of Conduct - Community guidelines
- Development Setup - Development environment
| Award | Year | Category |
|---|---|---|
| Best AI Music Project | 2024 | Open Source Awards |
| Innovation in Music Tech | 2024 | Music Technology Conference |
| Cultural Preservation | 2024 | Heritage Technology Awards |
- Basic raga detection
- Cross-platform support
- User authentication
- Real training data integration
- Music generation
- Advanced analytics
- Social features
- Mobile app optimization
- More raga support
- Advanced ML models
- Performance optimization
- API marketplace
- Multi-language support
- Cultural adaptations
- Enterprise features
- Research partnerships
Built with ❤️ for the Indian classical music community
Special thanks to:
- Indian Classical Music Community - For inspiration and cultural context
- Open Source Contributors - For amazing tools and libraries
- Lynx Framework Team - For cross-platform technology
- Convex Team - For real-time backend solutions
- Research Community - For academic foundations
← Back to Repository | Getting Started →
Last updated: January 2024