Emotion-Driven Music Recommendation System Using Multimodal AI

References

[1] Li, X., Wang, Z., and Zhang, Y. (2019). Emotion recognition from text using deep learning techniques. IEEE Access, 7, 123456–123467.

[2] Zhang, S., Zhao, X., and Tian, Y. (2020). Facial expression recognition using convolutional neural networks for emotion detection. IEEE Transactions on Affective Computing, 11(4), 789–799.

[3] Huang, C., Li, H., and Chen, Y. (2018). Music recommendation based on emotion recognition using machine learning techniques. International Journal of Multimedia Information Retrieval, 7(3), 185–195.

[4] Satt, A., Rozenberg, S., and Hoory, R. (2019). Efficient emotion recognition from speech using deep neural networks. Interspeech Conference Proceedings, 3297–3301.

[5] Zhao, Z., and Xu, B. (2021). Multimodal emotion recognition using audio, visual, and text information. IEEE Transactions on Multimedia, 23, 175–188.

[6] Sharma, A., and Dey, S. (2020). Emotion-based music recommendation system using machine learning algorithms. Procedia Computer Science, 167, 235–244.

[7] Kim, J., and Andre, E. (2018). Emotion recognition based on physiological and behavioral signals. ACM Transactions on Interactive Intelligent Systems, 8(2), 1–27.

[8] Poria, S., Cambria, E., Hazarika, D., and Vij, P. (2019). A deeper look into sarcasm detection using multimodal sentiment analysis. Information Processing & Management, 56(3), 1054–1066.

[9] Deng, J., Xu, X., Zhang, Z., and Schuller, B. (2021). Deep learning for emotion recognition: A review. IEEE Transactions on Affective Computing, 12(4), 1012–1029.

[10] Chen, L., Mao, X., and Xue, Y. (2018). Speech emotion recognition: Features and classification models. Digital Signal Processing, 78, 1–16.

[11] Wang, H., Chen, X., and Liu, Y. (2022). Deep learning based facial emotion recognition for human-computer interaction. Neurocomputing, 470, 87–96.

[12] Liu, Q., Wu, S., and Wang, L. (2020). Personalized music recommendation using emotional context awareness. IEEE Access, 8, 123889–123899.

[13] Kosti, R., Alvarez, J. M., Recasens, A., and Lapedriza, A. (2019). Emotion recognition in context using deep neural networks. CVPR Workshops, 123–130.

[14] Singh, R., and Kumar, P. (2021). Emotion-aware recommendation systems for multimedia content. Journal of Intelligent Information Systems, 56(2), 345–360.

[15] Huang, Y., and Wu, C. (2023). Multimodal emotion recognition for intelligent music recommendation systems. IEEE Access, 11, 45678–45689.

@article{1715489,
      author = {Rahulraagav M R, Dr. S. Parthasarathy},
      title = {Emotion-Driven Music Recommendation System Using Multimodal AI},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {2349-2356},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715489.pdf},
      abstract = {Emotion plays a crucial role in influencing human preferences, particularly in music selection. This project presents an Emotion-Based Music Recommendation System that uses artificial intelligence to analyze a user’s emotional state and provide personalized music suggestions. The system supports text, voice, and video inputs, enabling multimodal emotion detection for improved accuracy. Text and voice inputs are processed using natural language processing and speech-to-text techniques (Whisper), while video inputs are analyzed using computer vision for facial emotion recognition. Based on the detected emotion, the system recommends suitable music tracks using Spotify and YouTube Music APIs. The application is developed using Streamlit, integrating deep learning models and external APIs to deliver an interactive and user-friendly experience. Overall, the system enhances personalization, user engagement, and emotional well-being, demonstrating the application of AI in affective computing and recommendation systems.},
      keywords = {Emotion Detection, Multimodal Emotion Recognition, Music Recommendation, Natural Language Processing, Speech Recognition, Facial Expression Analysis, Deep Learning.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715489}
  }