EMOTION-AWARE ADAPTIVE MUSIC RECOMMENDATION SYSTEM USING REAL-TIME AFFECTIVE STATE ANALYSIS

Harish  Barapatre; Vishal Santosh  Barguje; Pratiksha Shreesahil  Vacche; Abhishek Vilas  Chaudhari

doi:10.29121/ijoest.v10.i2.2026.755

Authors

Dr. Harish Barapatre Associate Professor, Department of Computer Engineering, Yadavrao Tasgaonkar Institute of Engineering and Technology, Bhivpuri Road Karjat, Maharashtra 410201, India
Vishal Santosh Barguje Student, Department of Computer Engineering, Yadavrao Tasgaonkar Institute of Engineering and Technology, Bhivpuri Road Karjat, Maharashtra 410201, India
Pratiksha Shreesahil Vacche Student, Department of Computer Engineering, Yadavrao Tasgaonkar Institute of Engineering and Technology, Bhivpuri Road Karjat, Maharashtra 410201, India
Abhishek Vilas Chaudhari Student, Department of Computer Engineering, Yadavrao Tasgaonkar Institute of Engineering and Technology, Bhivpuri Road Karjat, Maharashtra 410201, India

DOI:

https://doi.org/10.29121/ijoest.v10.i2.2026.755

Keywords:

Emotion Recognition, Music Recommendation System, Affective Computing, Machine Learning, Human-Computer Interaction, Adaptive Systems

Abstract

Emotion plays a critical role in human–music interaction, influencing listening behavior, mood regulation, and cognitive engagement. Existing music recommendation systems, such as those used in Spotify and Apple Music, primarily rely on historical user preferences, collaborative filtering, or genre-based classification, which fail to capture the dynamic and real-time emotional states of users. This limitation results in suboptimal personalization and reduced user satisfaction.

This paper proposes an Emotion-Aware Adaptive Music Recommendation System that integrates real-time affective state detection with intelligent music mapping. The framework utilizes multimodal inputs such as facial expressions, textual sentiment, or physiological cues to infer user emotions and dynamically adjust music recommendations. A structured pipeline is designed to process emotional signals, compute emotion intensity scores, and map them to suitable music features such as tempo, genre, and energy levels.

Unlike traditional systems, the proposed approach emphasizes context-aware personalization, enabling continuous adaptation to changing user emotions. The system is conceptualized with a mathematically grounded scoring mechanism and an interpretable decision layer to ensure transparency and robustness. The proposed framework contributes to the advancement of affective computing in entertainment systems and provides a foundation for next-generation intelligent media platforms.

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