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Emotion Based Music Recommendation System Using Machine Learning and AI

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Emotion Based Music Recommendation System Using Machine Learning and AI


Parag Pardhi | Sakshi Deshmukh | Dr. Suman Sen Gupta



Parag Pardhi | Sakshi Deshmukh | Dr. Suman Sen Gupta "Emotion Based Music Recommendation System Using Machine Learning and AI" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-5, October 2024, pp.329-336, URL: https://www.ijtsrd.com/papers/ijtsrd69367.pdf

Music plays a significant role in influencing and reflecting human emotions. Traditional music recommendation systems, however, often fail to consider the listener's emotional state, leading to less personalized user experiences. An emotion-based music recommendation system that leverages artificial intelligence (AI) and machine learning (ML) techniques to identify and respond to user emotions. The system utilizes facial expression analysis and natural language processing to detect emotions in real-time. A recommendation algorithm then matches these emotions with appropriate music tracks, drawing from a diverse music database. Experimental results demonstrate that the emotion-based recommendation system significantly improves the accuracy of recommendations and user satisfaction compared to standard recommendation methods. The findings suggest that incorporating emotional context into music recommendation systems can enhance personalization and user engagement. Future research directions include expanding the system's emotion detection capabilities through multi-modal input and exploring real-time user feedback for dynamic adjustments.The project will commence with data collection from various sources, including APIs from platforms like Spotify and Genius, to gather song metadata, lyrics, and audio characteristics. We will employ advanced NLP techniques to analyze sentiment and categorize songs into emotions such as happiness, sadness, energy, calmness, and anger.

Emotion recognition, music recommendation, AI, machine learning, facial expression analysis, NLP, personalization, sentiment analysis, real-time detection


IJTSRD69367
Volume-8 | Issue-5, October 2024
329-336
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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