🎵 Mood Detection from Music

An AI-powered project that detects the mood of a listener based on the music they are playing. This system analyzes audio features from a song to classify the listener’s emotional state as one of the following moods: Happy, Sad, Energetic, or Calm.

🧠 Project Overview

This project uses machine learning to map audio features of music to emotional states. It’s ideal for building smart playlist generators, mood-aware applications, and personalized music recommendations.

📌 Key Features

• 🎶 Extracts relevant audio features (tempo, energy, valence, etc.)
• 🧠 Uses a trained ML model to predict mood
• 📊 Real-time mood classification (in supported UI)
• 🔌 API-ready architecture for integration
• 🌐 Planned React frontend (optional)

🛠️ Tech Stack

• Python (NumPy, pandas, scikit-learn)
• Librosa for audio analysis
• Matplotlib/Seaborn for visualization
• Google Colab for prototyping
• ReactJS frontend

🧩 How It Works

1. Extract features from the input audio (e.g., from Spotify or uploaded .mp3):
   • Tempo, Energy, Loudness, Spectral Contrast, etc.
2. Clean and scale the features.
3. Feed them into a trained ML model.
4. Output one of the 4 moods:
   • 😊 Happy
   • 😔 Sad
   • 😌 Calm
   • ⚡ Energetic