Year: 2024
Type: article
Abstract: In an increasingly fast-paced world, individuals often struggle to find things matching their current emotional state, leading to inefficiencies in emotional regulation, stress relief, and overall mental well-being. Traditional music recommendation systems rely on user inputs or previous playlist, which may not accurately capture a person’s real-time mood. There is a need for a more intuitive and responsive solution that can automatically detect and respond to an individual’s emotions to enhance their music listening experience. A Flask web application that utilizes facial expression recognition (FER) to detect emotions in real-time and recommend music that aligns with the detected mood. By integrating the FER 2013 dataset and Spotify API, system provides a personalized and dynamic music recommendation experience. Additionally, implemented a generative AI to deliver engaging content. The aim is to increase user retention and engagement by enriching theoverall music listening experience.
Author: Sushil Kumar
Institution: Maharaja Surajmal Institute of Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405977620