Enhanced Music Recommendation System

Authors

  • Manyu Manchala  Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
  • Abhinaya Vankadari  Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
  • Yenugu Keerthana  Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
  • Dr. Sunil Bhutada  Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India

Keywords:

Abstract

Music has a great connection with a person's emotions. It is very baffling for an individual to choose which music to listen to from a huge collection which already exists. As many of us know, humans show their feelings by expressions on their faces. The purpose of this project is to suggest a playlist by capturing the facial expression from the human face and helps in analysing the emotion. Detecting an emotion is one of the trending research topics. It culminates in understanding the current emotional state of the user. Here we use the K-means clustering algorithm for classifying songs and Haar cascade algorithm to recognize emotion. The image which is captured through video and the emotion detected by algorithms helps to display a list of songs. Based on the mood and emotion It will suggest songs to the user by matching their emotions to the song mood type so that It will save user searching time.

References

  1. https://towardsdatascience.com/
  2. https://www.ijert.org/
  3. Submitted to rgitkottayam Anagha S.Dhavalikar and Dr. R. K. Kulkarni, “Face Detection and Facial Expression Recognition System” 2014 Interntional Conference on Electronics and Communication System (ICECS -2014).
  4. http://www.igi-global.com/
  5. Intelligent Systems Technologies and Applications 2016", Springer Science and Business Media LLC, 2016
  6. Machine Learning and Information Processing", Springer Science and Business Media LLC, 2021
  7. Submitted to Institute of Research & Postgraduate Studies, Universiti Kuala Lumpur
  8. sersc.org
  9. idus.us.es
  10. Yong-Hwan Lee , Woori Han and Youngseop Kim, “Emotional Recognition from Facial Expression Analysis using Bezier Curve Fitting” 2013 16th International Conference on Network-Based Information Systems.
  11. Ahmed Aly Ibrahim, Zainhom Sayed Ghareeb, Omar M. Shehata, El-Sayed Imam Morgan. "A Robotic Surveillance Platform Based on an On-board Computer Vision Approach", Proceedings of the 4th International Conference on Control, Mechatronics and Automation - ICCMA '16, 2016
  12. F. Abdat, C. Maaoui and A. Pruski, “Humancomputer interaction using emotion recognition from facial expression”, 2011 UKSim 5th European Symposium on Computer T.-H. Wang and J.-J.J. Lien, “Facial Expression Recognition System Based on Rigid and Non-Rigid Motion Separation and 3D PoseEstimation,” J. Pattern Recognition, vol. 42, no. 5, pp. 962-977, 2009.
  13. Renuka R. Londhe, Dr. Vrushshen P. Pawar, “Analysis of Facial Expression and Recognition Based On Statistical Approach”, International Journal of Soft Computing and Engineering (IJSCE) Volume-2, May 2012.
  14. Anukriti Dureha “An Accurate Algorithm for Generating a Music Playlist based on Facial Expressions” : IJCA 2014. Bruce Ferwerda and Markus Schedl “Enhancing Music Recommender Systems with Personality Information and Emotional States”: A Proposal: 2014.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2022-06-30

Issue

Volume 9, Issue 3, May-June-2022

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Manyu Manchala, Abhinaya Vankadari, Yenugu Keerthana, Dr. Sunil Bhutada, " Enhanced Music Recommendation System, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 3, pp.371-376, May-June-2022.