🎶 AUDIORA: Music Recognition Application

Shazam-like Audio Identification Tool

📌 Introduction

AUDIORA is an advanced music recognition system designed to identify songs from short audio clips. Inspired by platforms like Shazam, it leverages cutting-edge audio fingerprinting, real-time processing, and a full-stack web interface to bridge the gap between user experience and embedded audio recognition technology.

Built with a modern React frontend, a powerful Go-based backend, and a Python audio engine, AUDIORA provides a scalable and responsive solution for music lovers and developers alike.

🧠 Project Overview

AUDIORA converts recorded audio into unique fingerprints, matches them against a fingerprint database, and returns the matching song metadata to the user — all through a user-friendly web interface. It supports live recordings, precise fingerprinting, and fast identification optimized for cloud-scale deployment.

✨ Features

• 🎤 Real-time audio capture from browser
• 🎼 Audio fingerprinting using FFT-based frequency analysis
• 🔎 Accurate song matching via Locality-Sensitive Hashing (LSH)
• 🧩 Modular backend in Go + Python
• 🌐 Modern, responsive frontend using React and Tailwind CSS
• 🧪 Basic testing using Postman, MongoDB Compass, and logs
• ☁️ Scalable and containerized using Docker

🧰 Technologies Used

Layer	Tools & Languages
Frontend	React.js, Tailwind CSS, Web Audio API
Backend (API)	Golang (Go), Flask (Python bridge)
Audio Engine	Python, Librosa, NumPy, SciPy, FFmpeg
Database	MongoDB
Containerization	Docker
Testing Tools	Postman, MongoDB Compass

📁 Project Structure

AUDIORA/
├── Backend/
│ ├── audio_engine/           # Python audio fingerprinting logic
│ ├── app.go                  # Go server API
│ └── flask_bridge.py         # Bridge to Python engine
├── Frontend/
│ ├── src/                    # React components
│ └── index.html
├── Dockerfile
├── database/                 # MongoDB fingerprint collections
├── tests/                    # Postman/API tests
└── README.md

🛠️ Recognition Workflow

1. Record: User records audio via browser.
2. Preprocess: Audio cleaned, converted to mono WAV using FFmpeg.
3. Fingerprinting: Spectral peaks extracted using FFT; hashes generated.
4. Match: Fingerprints matched against MongoDB using LSH.
5. Result: Matching song displayed with title, artist, and album.

🚀 How to Run

📥 Clone Repo

  git clone https://github.com/rakshitnarang018/Audiora01
  cd Audiora01

🧠 Backend Setup

1. Set up Python environment:

  python -m venv .venv
  source .venv/bin/activate      # On Linux/Mac
  .venv\Scripts\activate         # On Windows

2. Install Python dependencies:

  pip install -r requirements.txt

3. Run the Flask backend:

  cd Backend
  python app.py

Flask server will run at: http://localhost:5000

🎨 Frontend Setup

You can either run the frontend with npm or use Docker Compose:

  cd Frontend
  npm install
  npm run dev

Visit http://localhost:3000 in your browser to use the app.

🐳 Docker Deployment

AUDIORA uses Docker Compose to containerize and serve the React frontend, while the backend is run manually on your local machine.

📁 Prerequisites

• Docker & Docker Compose installed: Get Docker

🚀 Build and Start the Frontend

From the root of your project (where docker-compose.yml is present or create it as shown below):

  docker-compose up --build

🧪 Testing

Basic testing was performed using:

• ✅ Postman – for API request testing
• ✅ MongoDB Compass – to inspect stored fingerprints
• ✅ Temporary audio logs – for audio integrity validation
• ✅ Manual logs – to verify backend/frontend flow

🖼️ UI Snapshots

• 🎧 Landing Page – “Welcome to AUDIORA”
• 🟢 Recording Animation – “Listening for your tune...”
• 📊 Analyzing Page – “Matching your music...”
• 🎉 Result Page – Displays identified song details

🔭 Future Enhancements

• 🔁 Live-stream music detection
• 🌍 Multilingual/global music database
• ⚡ Edge computing for ultra-low latency
• 🔒 Audio encryption and user profile sync
• 🤖 AI-based music genre or mood tagging

📚 Acknowledgments

• Librosa, SciPy, FFmpeg, Flask, MongoDB
• Shazam – for inspiring the concept
• React + Tailwind community
• Python and Go open-source ecosystems