Abstract
Emotion plays a crucial role in influencing human preferences, particularly in music selection. This project presents an Emotion-Based Music Recommendation System that uses artificial intelligence to analyze a user’s emotional state and provide personalized music suggestions. The system supports text, voice, and video inputs, enabling multimodal emotion detection for improved accuracy. Text and voice inputs are processed using natural language processing and speech-to-text techniques (Whisper), while video inputs are analyzed using computer vision for facial emotion recognition. Based on the detected emotion, the system recommends suitable music tracks using Spotify and YouTube Music APIs. The application is developed using Streamlit, integrating deep learning models and external APIs to deliver an interactive and user-friendly experience. Overall, the system enhances personalization, user engagement, and emotional well-being, demonstrating the application of AI in affective computing and recommendation systems.
Keywords
Emotion Detection, Multimodal Emotion Recognition, Music Recommendation, Natural Language Processing, Speech Recognition, Facial Expression Analysis, Deep Learning.
Citations
IRE Journals:
Rahulraagav M R, Dr. S. Parthasarathy "Emotion-Driven Music Recommendation System Using Multimodal AI" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 2349-2356 https://doi.org/10.64388/IREV9I9-1715489
IEEE:
Rahulraagav M R, Dr. S. Parthasarathy
"Emotion-Driven Music Recommendation System Using Multimodal AI" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715489
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