The Mood Wave Music Player utilizes machine learning algorithms to assess mood descriptions and activity data. This project integrates technology with the power of music to create a meaningful and personalized journey for each user. Mood Wave Music Player is an emotion-driven music recommendation system designed to enhance user experience and emotional well-being. By using advanced machine learning techniques, it curates personalized playlists based on the user’s real-time mood, captured through facial expressions. This project aims to bridge the gap between emotions and music preferences, offering a seamless and adaptive listening experience.
🚀 Features Emotion Detection: Uses Convolutional Neural Networks (CNNs) to recognize emotions such as happy, sad, neutral, or angry with high accuracy. Real-Time Playlist Generation: Dynamically curates playlists tailored to the user’s mood, promoting mental health and balance. User Interaction: Simple, user-friendly interface for mood input via facial recognition or manual selection. Learning and Adaptation: Improves playlist recommendations over time by analyzing user feedback and listening history.
🔧 Technologies Used Programming Language: Python Libraries: OpenCV for image processing, TensorFlow/Keras for deep learning, and other supporting Python libraries. Algorithm: CNN for emotion classification. Dataset: Trained using a robust dataset of facial expressions.
📖 How It Works Input: The system captures the user’s facial expressions via a webcam or uses an image. Emotion Detection: Processes the input using CNNs to classify emotions into categories (e.g., happy, sad, neutral, or angry). Music Recommendation: Matches detected emotions with a music database to create a mood-specific playlist. Playback: Plays recommended songs while learning from user interactions for better future suggestions.
📊 Results and Performance Achieved an emotion detection accuracy of over 85%. Positive user feedback for personalization and adaptability. Potential applications in therapy, wellness, and entertainment.
🛠️ Future Enhancements Support for additional emotions (e.g., surprise, fear). Integration with popular music streaming services like Spotify Music. Real-time emotion tracking and playlist adaptation. Voice-controlled navigation and more robust emotion models.
🎯 Motivation and Impact This project is inspired by the emotional impact of music on mental health. Mood Wave Music Player aspires to create a personalized and adaptive listening experience, helping users navigate their emotions and enjoy music in a meaningful way.