Authors : Harsha Talele; Mrunal Bagal; Saniya Bhosale; Vishakha Awhale

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/mw4a5zza

Scribd : https://tinyurl.com/2twnzs28

DOI : https://doi.org/10.38124/ijisrt/26May1002

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Music recommendation systems based on emotions are an advanced way of getting individual media consumption, as they use artificial intelligence to understand and act on the emotional condition of users in real-time. The proposed research provides an AI-based architecture that recognizes human feelings based on facial expression analysis, using deep learning-based models like MobileNet and EfficientNet. The system effortlessly records live faces, recognizes emotionssuch as happiness, sadness, anger, neutrality, and surprise, and creates appropriate music suggestions at the same time. Unlike traditional methods of recommendations, where focus is mainly on historical listening history, the presented approach is dynamically adjusted to the mood of the current user, which allows customizing the recommendations contextually. The system can identify and match identified emotions with relevant genres and playlists, which increases user engagement and satisfaction with listening. The design focuses on computational performance as well as predictive power, thereby making sure it is practical to use in real time. An inclusive and culturally relevant recommendation is also facilitated by a multilingual and wide variety of music repositories. Future enhancement of multimodal signals, like physiological or behavioral signals, may also be incorporated into the framework to enhance emotional understanding. The findings of the experiments prove the accurate emotion recognition and the achievement of mood-based mapping of music. Despite the still remaining issues concerning the variability of the environment and matters regarding privacy, the given approach helps to emphasize the opportunities of intelligent, emotion-aware systems to improve the experience of listening to music online.

Keywords : Emotion Recognition, Multimodal System, Recommendation System, Mobilenet, Efficientnet, Hospitalized Patients, Artificial Intelligence, Machine Learning, Healthcare Technology.

References :

1. Thombare, M. B., & Gumaste, S. V., “Enhancing Facial Emotion Recognition with Vision Transformers, EfficientNet, and Domain Adaptation for Robust and Interpretable Models,” in Proceedings of the 2025 International Conference on Future Technologies (ICFT), IEEE, 2025.
2. S. Sharma, S. Avasthi, and I. Malik, “ Lightweight facial expressions analysis with MobileNet” in Proc. 3 rd Int. Conf. on Advancement in Computation and Computer Technologies (InCACCT),IEEE, 2025.
3. A. Poongodai, V. Bhavitha, D. Hemanth, G. Jahnavi, and S. Huzma, Human emotion detection based on real-time facial expression by deep learning, in Proc. 6th Int. Conf. on Inventive Research in Computing Applications (ICIRCA), 2025.
4. P. Utami, R. Hartanto, and I. Soesanti, The EfficientNet performance of facial expressions recognition, in Proc. 5 th Int. Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2022.
5. Sankaradass, V, “Facial Emotion Classification Utilizing EfficientNet: A Comprehensive Deep Learning Method,” in Proceedings of the International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG), IEEE, 2024.
6. S. G. Shaila, D. Shivamma, K. Tejashree, and U. S. Monica,“Facial expression recognition of compound emotions with MobileNet architecture, in Proc. Int. Conf. on Artificial Intelligence and Data Engineering (AIDE), 2022.
7. S. Priyadharshini, Music recommendation system using facial emotion analysis, in procs. Intelligent controls and computers systems Intelligent computing and control systems conference (ICICCS), 2025.
8. S. Oviya et al., GANs-based music generation: Unlocking the power of music in Proc. 3rd Int. Conf. on Augmented Intelligence and Sustainable Systems (ICAISS), 2025. ISBN: 979-8-3315-0724-4
9. M. S. Guthula et al., Music recommendation system, opinion based on facial emotion analysis, in Proc. Int. Conf. on Emerging Techniques in Computational Intelligence (ICETCI), Aug. 2024.
10. R. Renugadevi et al., “Deep learning based on facial emotion to recommend YouTube music, Proc. 2nd Int. Conf. on Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), 2024.
11. N. Gupta et al., “Face emotion recognition based intelligent music recommendation system, in Proc. Int. Conf. on Computing, Communication, and Intelligent Systems (ICCCIS), 2023.
12. Harshvardhan et al., “Aspect-based music recommendation system with CNN in Proc. 4 th Int. Conf. on Technological Advancements in Computational Sciences, Nov. 2024.
13. Mingwei Wu, “Analysis of user music behavior and emotion technology in intelligent music system design,” in Proceedings of the International Conference on Integrated Intelligence and Communication Systems (ICIICS), IEEE, 2023.
14. V. P. Sharma, “Emotion-based music recommendation system (in Proc. 9th Int. Conf. on Reliability, Infocom Technologies and Optimization (ICRITO)) 2021.
15. P. Kalairasi, M. C. Prabhu, and S. S. Datta, Music recommendation on real-time emotions with deep learning in the Proc. Int. Conf. on Inventive Computing and Informatics (ICICI), Feb. 2025, pp. 675680.doi: 10.1109/ICMACC62921.2024.10894134.
16. [16] A. Srivastava, D. K. Srivastava, and M. Shandilya, “Facial emotion-based music recommender system using CNN, in Proc. Int. Conf. on Artificial Intelligence and Smart Communication (AISC), Jan. 2023, pp. 113118. doi: 10.1109/AISC56616.2023.10085294.
17. K. Vayadande, P. Narkhede, and R. Thakur, Facial emotion-based song recommendation system, in Proc. Int. Conf. on Computational Intelligence and Sustainable Engineering Solutions (CISES), Apr. 2023, pp. 240248.doi: 10.1109/CISES58720.2023.10183606.
18. P. Ashwini, P. Dammalapati, N. Ramineni, and T. Adilakshmi, “Facial expression-based music recommendation system using deep convolutional neural network in Proc. Int. Conf. on Expert Clouds and Applications (ICOECA), Apr. 2024, pp. 992, 999.doi: 10.1109/ICOECA62351.2024.00173.
19. V. S. G. S. P. Bottu and K. Ragava, “Emotion-based music recommendation system that combines facial expression recognition and lyrics sentiment analysis in IEEE access, vol. 13, 2025.doi: 10.1109/ACCESS.2025.3570011.
20. S. Gowda and P. Badrinath, “Emotion-based music recommendation system using machine learning,” in Proc. 2024 Int. Conf. on IoT, Communication and Automation Technology (ICICAT), IEEE, 2024, doi: 10.1109/ICICAT62666.2024.10923478.
21. T. P. V. Tejaswini and Y. R. Reddy, A novel emotion-based music recommendation system using CNN, in Proc. 7 th Intelligent Computing and Control Systems (ICICCS), 2023.
22. P. Tejaswini, D. Abhiram, C. Yasaswini, G. S. Babu, and D. H. S. Rao, “Music recommendation based on real-time emotion detection,” International Journal of Gender, Science and Technology, vol. 13, no. 1, Mar. 2024.
23. M. S. Guthula et al., Recommendation system: Facial emotion analysis based music recommendation, in Proc. Int. Conf. on Emerging Techniques in Computational Intelligence (ICETCI), 2024.
24. M. Athavle et al., Music recommendation with facial emotions, Journal of Informatics Electrical and Electronics Engineering, 2021.
25. J. Odii, “Emotion detection with a bidirectional long short-term memory (BiLSTM), International Journal of Current Pharmaceutical Review and Research, Nov. 2023.
26. K. Vaydande, “Recent progress in emotion-based song recommendation system based on facial recognition data of users on device-independent system: Facial emotion-based song recommendation system in Proc. Int. Conf. on Computational Intelligence and Sustainable Engineering Solutions (CISES), 2023.
27. P. Yadav, V. Kumar, V. Ranga, and R. M. Rawat, “Analysis of facial sentiments: A deep-learning way in Proc. Int. Conf. on Electronics and Sustainable Communication Systems (ICESC), 2020.
28. P. Kaviya and T. Arumugaprakash, Group facial emotion analysis system using convolutional neural network in Proc. Int. Conf. on Trends in Electronics and Informatics (ICOEI), 2020.
29. S. Jaiswal and G. C. Nandi, “Robust real-time emotion detection system using CNN architecture,” Neural Computing and Applications, 2020.
30. K. Patel et al., The article, Facial sentiment analysis using AI techniques: State-of-the-art, taxonomies, and challenges at IEEE Access, 2020.
31. G. Negret, A. Salceanu, and A. Vizireanu, “A music recommendation system based on deep learning and facial emotion recognition,” in Proc. 10th Int. Conf. on Robotica (RoCHI), IEEE, 2022.
32. P. Pardhi, A. R. Bhosale, R. R. Londhe, S. R. Patil, and K. S. Wani, “Emotion-based music recommendation system using machine learning and AI,” International Journal of Trend in Scientific Research and Development (IJTSRD), 2024.
33. A. K. Negi, “Automatic music recommendation system algorithm using facial expression based on machine learning,” International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2024.
34. K. Vaydande et al., “Facial emotion-based song recommendation system," in Proc. Int. Conf. on Computational Intelligence and Sustainable Engineering Solutions (CISES) 2023.
35. R. Ramanathan, R. Kumaran, R. R. Gupta, and V. Prabhu, An intelligent music player based on emotion recognition, in Proc. 2nd Int. Conf. on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), 2017.