This project is a simplified implementation of the Shazam audio fingerprinting algorithm in Go.
- Audio Fingerprinting: Extracts unique hashes from audio samples for quick lookup.
- Spectrogram Generation: Converts audio signals into a time-frequency representation.
- Peak Detection: Identifies significant points in the spectrogram for robust matching.
- Database Matching: Compares extracted fingerprints against a database to recognize audio.
- Go Implementation: Uses idiomatic Go and best practices for efficient processing.
- Preprocessing: The audio file is converted into a spectrogram.
- Peak Extraction: Unique peak points are identified in the spectrogram.
- Fingerprinting: Hashes are generated from peak pairs.
- Storage & Matching: Hashes are stored and compared for identification.
- Recognition: The system returns the best-matching audio file from the database.
git clone https://github.com/yourusername/shazam-go
cd shazam-go
go mod tidy
- Go
- PortAudio (for audio processing)
- go-audio/wav (for reading wv file)
- A database (e.g., PostgreSQL, SQLite) for fingerprint storage
- Noise Reduction: Improve robustness against background noise.
- Real-time Recognition: Implement streaming audio recognition.
- Distributed Processing: Optimize for large-scale audio databases.
Contributions are welcome! Feel free to submit pull requests and issues.