AuraBeat is a deep learning-based music recommendation system that tailors playlists to your emotional state — all in real-time — using facial expression recognition. It leverages CNN architectures like ResNet50V2 and VGG16 to detect emotions from your face and recommends songs accordingly.
- 📖 Overview
- 🧠 How It Works
- 🛠 Tech Stack
- 🚀 Getting Started
- 📊 Results
- 📎 Files
- 📚 References
- 📌 Future Work
“Music is what emotions sound like.”
AuraBeat brings emotional intelligence to music players by recommending songs that resonate with your current mood, not just your listening history.
Whether you're smiling after a good day or looking stressed from a deadline — AuraBeat catches your emotion and queues the right playlist.
-
Emotion Detection:
- Uses facial expression recognition via a webcam.
- Detects emotions like:
Happy,Sad,Angry,Neutral,Surprise, etc.
-
Deep Learning Models:
- Custom CNN (66% accuracy)
- ResNet50V2 (68.5% accuracy)
- VGG16 (65% accuracy)
-
Music Mapping:
- Each emotion is mapped to a song mood (from Spotify dataset).
- Top 5 songs are recommended based on popularity + emotional alignment.
| Detected Emotion | Song Mood |
|---|---|
| Happiness | Happy |
| Sadness, Disgust | Sad |
| Anger, Fear | Calm |
| Surprise, Neutral | Energetic |
- 🧠 TensorFlow / Keras (ResNet50V2, VGG16)
- 🖼 OpenCV (facial detection)
- 📊 Pandas, NumPy (data handling)
- 🎵 Spotify Mood Dataset
- 📁 FER2013 Emotion Dataset
- 🧪 Scikit-learn (F1 Score, Confusion Matrix)
Ensure you have Python 3.7+ and pip installed. Then install dependencies:
pip install -r requirements.txt- Launch the notebook:
jupyter notebook Auranewcode.ipynb
- Run each cell in sequence to:
- Load and preprocess data
- Train or load models
- Run emotion detection on webcam/image
- Get top 5 song recommendations
| Model | Accuracy |
|---|---|
| ResNet50V2 | 68.5% |
| CNN (Custom) | 66% |
| VGG16 | 65% |
- High precision for
Happiness,Surprise - Some overlap between
Anger&Sadnessdue to similar facial features
| File | Description |
|---|---|
Auranewcode.ipynb |
Main Jupyter Notebook with full pipeline |
Auranew.pdf |
Project Report / Technical Documentation |
requirements.txt |
Dependencies list (to be added) |
screenshots/ |
(Optional) Visual outputs or UI demos |
Includes work inspired by:
- FER2013 Dataset
- Spotify Audio Features
- ResNet / VGG architectures
- Emotion models (Thayer’s valence-arousal plane)
Full list in Auranew.pdf
- Build a live web app interface (Streamlit or Flask)
- Integrate Spotify API for real playback
- Improve accuracy with ensemble models
- Consider multi-modal emotion input (text, voice, etc.)
Thanks to the CSE(AIML) department for support and feedback throughout the project.