Skip to main navigation menu Skip to main content Skip to site footer
×
English | Español (España)
Editorial
Home
Original

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

By
Patakamudi Swathi ,
Patakamudi Swathi

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India.

Search this author on:

PubMed | Google Scholar
Dara Sai Tejaswi ,
Dara Sai Tejaswi

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India.

Search this author on:

PubMed | Google Scholar
Mohammad Amanulla Khan ,
Mohammad Amanulla Khan

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India.

Search this author on:

PubMed | Google Scholar
Miriyala Saishree ,
Miriyala Saishree

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India.

Search this author on:

PubMed | Google Scholar
Venu Babu Rachapudi ,
Venu Babu Rachapudi

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India.

Search this author on:

PubMed | Google Scholar
Dinesh Kumar Anguraj ,
Dinesh Kumar Anguraj

Koneru Lakshmaiah Education Foundation, Department of CSE, Vaddeswaram, Andhra Pradesh, India.

Search this author on:

PubMed | Google Scholar

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.

How to Cite

1.
Swathi P, Sai Tejaswi D, Amanulla Khan M, Saishree M, Babu Rachapudi V, Kumar Anguraj D. A research on a music recommendation system based on facial expressions through deep learning mechanisms. Gamification and Augmented Reality [Internet]. 2024 Apr. 29 [cited 2024 Jul. 18];2:38. Available from: https://gr.saludcyt.ar/index.php/gr/article/view/38

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

Article metrics

Google scholar: See link

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.