ST-Shazam

Package Name: st-shazam (npm compatible) | Display Name: ST Shazam

A Node.js implementation of Shazam-like audio recognition using advanced audio fingerprinting algorithms. This project can identify songs from audio files by generating acoustic fingerprints and matching them against the Shazam database.

🎵 Features

• Audio Recognition: Identify songs from audio files (MP3, WAV, etc.)
• Audio Fingerprinting: Generate unique acoustic signatures using FFT and peak detection
• Shazam API Integration: Match fingerprints against Shazam's vast music database
• Audio Processing: Automatic conversion and normalization of audio files

📋 Requirements

• Node.js (v14 or higher)
• FFmpeg (automatically installed via @ffmpeg-installer/ffmpeg)

🚀 Installation

# Clone the repository
git clone https://github.com/sheikhtamimlover/ST-Shazam.git

# Navigate to project directory
cd ST-Shazam

# Install dependencies
npm install

💻 Usage

Command Line

# Recognize a song from an audio file
node ST.js <path_to_audio_file>

# Or use npm start
npm start ./audio.mp3

# Example
node ST.js ./audio.mp3

As a Module

const { recognizeSong } = require('st-shazam');
// Or if using locally: const { recognizeSong } = require('./index.js');

async function identify() {
  try {
    const result = await recognizeSong('./your-audio-file.mp3');
    
    if (result.matches && result.matches.length > 0) {
      console.log('Song found!');
      console.log('Title:', result.matches[0].track?.title);
      console.log('Artist:', result.matches[0].track?.subtitle);
    } else {
      console.log('No matches found');
    }
  } catch (error) {
    console.error('Recognition failed:', error);
  }
}

identify();

🔧 How It Works

1. Audio Processing: The input audio is converted to mono 16kHz PCM format using FFmpeg
2. Fingerprint Generation: Audio samples are analyzed using FFT to generate a unique acoustic signature
3. API Request: The fingerprint is sent to Shazam's API for matching
4. Result: Returns song metadata including title, artist, album, and more

📦 Dependencies

• axios: HTTP client for API requests
• uuid: Generate unique device and session IDs
• fluent-ffmpeg: Audio processing and format conversion
• @ffmpeg-installer/ffmpeg: FFmpeg binary installer
• fft.js: Fast Fourier Transform implementation

📁 Project Structure

ST-Shazam/
├── src/
│   ├── algorithm.js         # Fingerprint generation algorithm
│   ├── signature-format.js  # Signature encoding/decoding
│   └── hanning.js           # Hanning window function
├── ST.js                    # Main entry point (CLI tool)
├── index.js                 # Core module exports (recognizeSong, processAudio)
├── package.json             # Project configuration
└── README.md                # This file

🔑 API Information

This project uses Shazam's unofficial API with an included authentication token. The package works out of the box without requiring any additional setup or configuration.

🎯 Use Cases

• Music identification apps
• Audio recognition services
• Music discovery platforms
• Audio content analysis tools
• Educational projects on audio processing

⚠️ Limitations

• Recognition accuracy depends on audio quality
• Requires internet connection for API access
• API rate limits may apply
• Only identifies songs in Shazam's database

👨‍💻 Author

Sheikh Tamim

• Instagram: @sheikh.tamim_lover
• GitHub: @sheikhtamimlover

📄 License

MIT License - feel free to use this project for personal or commercial purposes.

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

🌟 Acknowledgments

• Shazam for their amazing music recognition technology
• The open-source community for the audio processing libraries

Made with ❤️ by Sheikh Tamim