Music Recommendation and Generation Based on Face Emotion Detection

To pioneer a personalized and emotionally resonant music recommendation platform, this study proposes a groundbreaking approach that integrates emotion detection and Long Short Term Memory(LSTM) for music generation. The system harnesses sophisticated emotion detection algorithms to analyze user emotions in real-time, extracting insights from textual inputs, speech tones, and facial expressions. Subsequently, leveraging a combination of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), music tailored to the identified emotional state is generated through an LSTM music production model. This real-time synthesis culminates in a customized playlist finely attuned to the user's emotional landscape. Continual refinement of recommendations is facilitated through user feedback, ensuring a fluid and adaptive music recommendation experience. The project's innovation lies in its fusion of LSTM-driven music creation with emotion detection, enhancing user satisfaction by delivering music that not only aligns with their emotional state but also resonates with their musical preferences. The efficacy of this approach in providing emotionally appropriate musical recommendations is underscored through rigorous user studies and comparisons with conventional recommendation systems. By leveraging cutting-edge technology in emotion recognition and LSTM-based music generation, this initiative aims to redefine music recommendation systems, offering users a more personalized and immersive experience within music streaming platforms.