Facial Emotion-Based Song Recommender System Using CNN

Facial Emotion-Based Song Recommender System Using CNN
  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-6
Year of Publication : 2024
Author : Ashish Tripathi, Abhijat Mishra, Rajnesh Singh, Bhoopendra Dwivedy, Amit Kumar, Kuldeep Singh
DOI : 10.14445/22315381/IJETT-V72I6P129

How to Cite?

Ashish Tripathi, Abhijat Mishra, Rajnesh Singh, Bhoopendra Dwivedy, Amit Kumar, Kuldeep Singh, "Facial Emotion-Based Song Recommender System Using CNN," International Journal of Engineering Trends and Technology, vol. 72, no. 6, pp. 315-327, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I6P129

Abstract
It is observed that many times, people are not able to recognize what kind of song they really want to listen to on the basis of their current mood. Sometimes, people end up searching for the perfect song according to their mood, and it eventually leads to a waste of time in finding the exact requirements of the song. In the era of technology and research, specifically in the world of Artificial Intelligence (AI), implementing these technologies in the advancement of song recommender systems will help people recognize the exact requirements and recommend songs accordingly. It will be the perfect combination of technology and the requirements of the user. This research basically focuses on the recommendation of a song to the person based on his/her current mood. According to the mood of an individual, the song is recommended. The process goes like this: the user first takes a photo of the user with the help of a webcam on the laptop, with the user’s permission. After that, the number of photos is matched with the data stored, and when the particular emotion is identified with the help of a CNN (convolutional neural network), it is then redirected to YouTube. According to the mood of the user, the song is played. Hence, the basic requirement of the song recommender system is completed.

Keywords
Facial emotion, Convolutional Neural Network (CNN), Song recommendations, Emotion recognition.

References
[1] Hongli Zhang, Alireza Jolfaei, and Mamoun Alazab, "A Face Emotion Recognition Method Using Convolutional Neural Network and Image Edge Computing," IEEE Access, vol. 7, pp. 159081-159089, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Huihui Han et al., "Music Recommendation Based on Feature Similarity," 2018 IEEE International Conference of Safety Produce Informatization, Chongqing, China, pp. 650-654, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Shoto Sasaki et al., "Affective Music Recommendation System Reflecting the Mood of Input Image," 2013 International Conference on Culture and Computing, Kyoto, Japan, pp. 153-154, 2013.
[CrossRef] [Publisher Link]
[4] Chuan-Yu Chang et al., "A Music Recommendation System with Consideration of Personal Emotion," 2010 International Computer Symposium, Tainan, Taiwan, pp. 18-23, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Dongmoon Kim et al., "A Music Recommendation System with a Dynamic K-Means Clustering Algorithm," Sixth International Conference on Machine Learning and Applications, Cincinnati, OH, USA, pp. 399-403, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[6] K.M. Aswin et al., "HERS:Human Emotion Recognition System," 2016 International Conference on Information Science, Kochi, India, pp. 176-179, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Akriti Jaiswal, A. Krishnama Raju, and Suman Deb, "Facial Emotion Detection Using Deep Learning," 2020 International Conference for Emerging Technology, Belgaum, India, pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Deger Ayata, Yusuf Yaslan, and Mustafa E. Kamasak, "Emotion Based Music Recommendation System Using Wearable Physiological Sensors," IEEE Transactions on Consumer Electronics, vol. 64, no. 2, pp. 196-203, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Shlok Gilda et al., "Smart Music Player Integrating Facial Emotion Recognition and Music Mood Recommendation," 2017 International Conference on Wireless Communications, Signal Processing and Networking, Chennai, India, pp. 154-158, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[10] K.S. Krupa et al., "Emotion Aware Smart Music Recommender System Using Two Level CNN," 2020 Third International Conference on Smart Systems and Inventive Technology, Tirunelveli, India, pp. 1322-1327, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Wei Chun Chiang, Jeen Shing Wang, and Yu Liang Hsu, "A Music Emotion Recognition Algorithm with Hierarchical SVM Based Classifiers," 2014 International Symposium on Computer, Consumer and Control, Taichung, Taiwan, pp. 1249-1252, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Ferdos Fessahaye et al., "T-RECSYS: A Novel Music Recommendation System Using Deep Learning," 2019 IEEE International Conference on Consumer Electronics, Las Vegas, NV, USA, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Davis Moswedi, and Ritesh Ajoodha, "Music Classification Using Fourier Transform and Support Vector Machines," 2022 International Conference on Engineering and Emerging Technologies, Kuala Lumpur, Malaysia, pp. 1-4, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Renata L. Rosa, Demsteneso Z. Rodriguez, and Graca Bressan, "Music Recommendation System Based on User's Sentiments Extracted from Social Networks," IEEE Transactions on Consumer Electronics, vol. 61, no. 3, pp. 359-367, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Xuan Zhu et al., "An Integrated Music Recommendation System," IEEE Transactions on Consumer Electronics, vol. 52, no. 3, pp. 917-925, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Manali Shaha, and Meenakshi Pawar, "Transfer Learning for Image Classification," 2018 Second International Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India, pp. 656-660, 2018.
[CrossRef] [Google Scholar] [Publisher Link]