A research on a music recommendation system based on facial expressions through deep learning mechanisms

Patakamudi Swathi, Dara Sai Tejaswi, Mohammad Amanulla Khan, Miriyala Saishree, Venu Babu Rachapudi and Dinesh Kumar Anguraj

Gamification and Augmented Reality, 2024, vol. 2, 38

Abstract: In this study, we propose a new music recommendation system (MRS) that combines facial expression recognition technology and deep learning algorithms to respond to the changing music industry environment and provide personalized music recommendations based on the user's emotional state. Our approach includes a thorough study of facial expression recognition, emotion-based music recommendation systems, and deep learning engines, as well as a detailed presentation of the MRS design, system architecture, and deep learning engines used. Through extensive experiments, we evaluate MRS's ability to accurately recognize facial expressions, filter music based on emotional states, and effectively recommend music to users. We analyze the results of follow-up experiments to identify the strengths and limitations of MRS compared to existing approaches, and conduct a comparative study with the latest music recommendation systems based on deep learning and emotion. This comparison highlights the originality and potential of the proposed MRS system to improve user experience and promote the development of artificial intelligence-based music recommendation systems. This study demonstrates the problem of accurately determining a user's emotional state from facial expressions, which requires the integration of facial expression recognition systems, deep learning, and music recommendation systems. Using advanced deep learning techniques and a comprehensive experimental setup, the proposed MRS provides a solution to this problem by facilitating accurate emotional state identification and personalized music recommendations. Overall, MRS represents a powerful and innovative response to the growing demand for accurate and reliable music recommendations and shows significant potential for future collaboration and development of AI-based music recommendation systems.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:gammif:v:2:y:2024:i::p:38:id:38

DOI: 10.56294/gr202438

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