Music Mood Based Recognition System Based on Machine Learning and Deep Learning
Keywords:
Machine Learning, Deep Learning, OpenCV, Music, Facial Recognition, MoodAbstract
There are extensive studies about music’s impact on human’s emotional state. Humans detect a wide range of emotions from various genres of music, and music plays an integral role in personality development and the treatment of ailments. Music has tremendous effects on human moods and thoughts. Consequently, it impacts cognitive and biological health, and the concept of well-being through music is acquiring traction. In the treatment of depression, music therapy gets witnessed as an addendum to psychoanalysis. Music can enhance intellectual and physical work, study, sports, relaxation, relieve fatigue, and music therapy, among other things. People often get confused while searching for music according to their interests and mood. Individuals usually listen to a particular genre or performer when they are in a certain mood. Music has the ability to control mood, specifically to boost energy, and reduce anxiety. Listening to the correct song at the opportune timing, may help with mental health. As a result, human mood changes and music have an interdependent affinity. In this paper, we aim to develop an application that can understand facial features (Mood and Emotions) and recommend music accordingly using Machine Learning and Deep Learning as tools and algorithms.
Downloads
References
Russell, James. (1980) “A Circumplex Model of Affect,” Journal of Personality and Social Psychology. 39. 1161-1178. 10.1037/h0077714.
Helmholz, Patrick & Meyer, Michael & Robra-Bissantz, Susanne. (2019)” Feel the Moosic: Emotion-based Music Selection and Recommendation” 10.18690/978-961-286-280-0.11.
Nguyen, Van & Kim, Donglim & Ho, V.P. & Lim, Younghwan. (2017)”A New Recognition Method for Visualizing Music Emotion,” International Journal of Electrical and Computer Engineering. 7. 1246-1254. 10.11591/ijece.v7i3.pp1246-1254.K.
Mustamin Anggo and La Arapu 2018 J. Phys.: Conf. Ser. 1028 012119
Deny John Samuvel, B. Perumal and Muthukumaran Elangovan, "Music rec-ommendation system based on facial emotion recognition", 2020.
K. Vikram and S. Padmavathi, "Facial parts detection using Viola Jones algorithm," 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), 2017, pp. 1-4, doi: 10.1109/ICACCS.2017.8014636.
Giannopoulos, P., Perikos, I., Hatzilygeroudis, I. (2018). Deep Learning Approaches for Facial Emotion Recognition: A Case Study on FER-2013. In: Hatzilygeroudis, I., Palade, V. (eds) Advances in Hybridization of Intelligent Methods. Smart Innovation, Systems and Technologies, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-319-66790-4_1
P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar and I. Matthews, "The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, 2010, pp. 94-101, doi: 10.1109/CVPRW.2010.5543262.
Ellen Goeleven, Rudi De Raedt, Lemke Leyman & Bruno Verschuere (2008) “The Karolinska Directed Emotional Faces: A validation study, Cognition and Emotion,” 22:6, 1094-1118, DOI: 10.1080/02699930701626582
Stewart Joanna, Garrido Sandra, Hense Cherry, McFerran Katrina, “Music Use for Mood Regulation: Self-Awareness and Conscious Listening Choices in Young People with Tendencies to Depression” JOURNAL=Frontiers in Psychology, VOLUME=10, YEAR=2019, DOI=10.3389/fpsyg.2019.01199, ISSN=1664-1078
Ahmad, Nawaz & Rana, Afsheen. (2015). Impact of Music on Mood: Empirical Investigation. Research on Humanities and Social Sciences. 5. 98-101.
McCraty, Rollin & Barrios-Choplin, B & Atkinson, M & Tomasino, Dana. (1998). The effects of different types of music on mood, tension, and mental clarity. Alternative therapies in health and medicine. 4. 75-84.
Ahmad, Nawaz and Rana, Afsheen, Impact of Music on Mood: Empirical Investigation (November 29, 2015). Research on Humanities and Social Sciences. ISSN (Paper) 2224-5766 ISSN (Online) 2225-0484 (Online), Available at SSRN: https://ssrn.com/abstract=2696883
Stewart J, Garrido S, Hense C, McFerran K. Music Use for Mood Regulation: Self-Awareness and Conscious Listening Choices in Young People With Tendencies to Depression. Front Psychol. 2019 May 24;10:1199. doi: 10.3389/fpsyg.2019.01199. PMID: 31178806; PMCID: PMC6542982.
A. Baharum, T. W. Seong, N. H. M. Zain, N. M. M. Yusop, M. Omar and N. M. Rusli, "Releasing stress using music mood application: DeMuse," 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea (South), 2017, pp. 351-355, doi: 10.1109/ICTC.2017.8191001.
Xue, H., Xue, L., Su, F. (2015). Multimodal Music Mood Classification by Fusion of Audio and Lyrics. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8936. Springer, Cham. https://doi.org/10.1007/978-3-319-14442-9_3
Campbell, E. A., Berezina, E., & Gill, C. M. H. D. (2021). The effects of music induction on mood and affect in an Asian context. Psychology of Music, 49(5), 1132–1144. https://doi.org/10.1177/0305735620928578
Miguel Civit, Javier Civit-Masot, Francisco Cuadrado, Maria J. Escalona,A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends,Expert Systems with Applications,Volume 209,2022,118190,ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.118190.
Garg, Anupam & Chaturvedi, Vybhav & Dhindsa, Arman Beer & Varshney, Vedansh & Parashar, Anshu. (2022). Machine learning model for mapping of music mood and human emotion based on physiological signals. Multimedia Tools and Applications. 81. 10.1007/s11042-021-11650-0.
S. Deebika, K. A. Indira and Jesline, "A Machine Learning Based Music Player by Detecting Emotions," 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, 2019, pp. 196-200, doi: 10.1109/ICONSTEM.2019.8918890.
Han, D., Kong, Y., Han, J. et al. A survey of music emotion recognition. Front. Comput. Sci. 16, 166335 (2022). https://doi.org/10.1007/s11704-021-0569-4
Dr. J Naga Padmaja, Amula Vijay Kanth, P Vamshidhar Reddy, B Abhinay Rao, Web Application for Emotion-Based Music Player using Streamlit, https://doi.org/10.22214/ijraset.2023.49019
S Metilda Florence and M Uma, "Emotional Detection and Music Recommendation System based on User Facial Expression”, IOP Conference Series: Materials Science and Engineering, vol. 912, no.6, pages:062007.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
