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Emotion-Driven Music Recommendation System Using Multimodal Data

Author

Listed:
  • Vyavhare V. A
  • Divekar S. N
  • Amol Chakane
  • Ganesh Rahinj
  • Kiran Kshirsagar

Abstract

The rapid advancement of Artificial Intelligence (AI) and Deep Learning (DL) has significantly transformed personalized content delivery systems. Traditional music recommendation systems rely primarily on user history and collaborative filtering, which fail to capture real-time emotional states. This paper presents an Emotion-Driven Music Recommendation System that leverages multimodal data such as facial expressions and speech signals to detect user emotions and provide adaptive music recommendations. Convolutional Neural Networks (CNNs) are used for facial emotion recognition, while Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models are applied for speech emotion analysis. The detected emotion is mapped to a hybrid recommendation engine combining content-based and emotion-adaptive filtering. Experimental results show an accuracy of 93.8% in emotion detection with real-time processing capability. The system enhances user engagement, personalization, and emotional well-being.

Suggested Citation

  • Vyavhare V. A & Divekar S. N & Amol Chakane & Ganesh Rahinj & Kiran Kshirsagar, 2026. "Emotion-Driven Music Recommendation System Using Multimodal Data," International Journal of Scientific Research in Artificial Intelligence and Machine Learning, International Journal of Scientific Research in Artificial Intelligence and Machine Learning, vol. 2(3), pages 137-144, May.
    • Handle: RePEc:jbo:ijsrml:v2:y2026:i3:id:67
    Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML26246
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