PERSONALIZED MUSIC RECOMMENDATION THROUGH MULTI-MODAL EMOTION RECOGNITION

We introduce, in this work, a holistic approach for music recommendation by multi-modal emotion recognition. With facial expressions, speech, and text sentiment analysis as the three inputs, our system dynamically matches the music recommendation with the emotional state of the user. This system ensures accuracy with real-time performance due to its use of the latest machine learning techniques, including CNNs, LSTMs, and pre-trained NLP models. Its effectiveness is well supported through experiments that compare it with conventional methods and provide a personalized and emotionally aware user experience.