Emotion-Driven Music Recommendation System Using Multimodal Data

Vyavhare V. A, Divekar S. N, Amol Chakane, Ganesh Rahinj and Kiran Kshirsagar

International Journal of Scientific Research in Artificial Intelligence and Machine Learning, 2026, vol. 2, issue 3, 137-144

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.

Keywords: Emotion Recognition; Music Recommendation; CNN; LSTM; Multimodal Learning (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML26246
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