AI-Driven Serverless Cloud Framework For Music Emotion-Based Business Intelligence And Visualization
Keywords:
Music Emotion Recognition, AI, Serverless Computing, Cloud Computing, Data Visualization, Accessibility, Business Intelligence, Neural Networks, Real-Time Systems, User ExperienceAbstract
Imagine a world where music isn't just heard, but truly felt and seen, especially for those who can't hear it. This paper introduces an exciting new framework that uses AI and serverless cloud technology to make music emotions visible in real-time. It's designed not only to boost accessibility for the hearing-impaired but also to offer smart business insights. Our system takes raw audio signals and transforms them into dynamic visual experiences that capture both the physical rhythm and the emotional heart of the music. We've built a clever neural network, our Music Emotion Recognition (MER) model, that pulls out these emotional cues and weaves them directly into the visuals.
By using serverless computing, we ensure the system is incredibly scalable, cost-effective, and available to everyone, adapting effortlessly to different users and demands. Plus, we've included an account management system and a way to collect user feedback. This means you can customize your experience, and our model keeps learning and getting better over time, becoming more accurate and personalized. Early results are really promising: our framework not only works effectively for real-time music visualization but also excels in emotion recognition, showing much lower errors and stronger correlations compared to existing methods, even with shorter music segments. This isn't just about making music more accessible; it's about giving businesses a powerful new way to understand the emotional impact of music, opening doors for personalized services and smarter marketing.
References
1. Aljanaki, A., Yang, Y.-H., & Soleymani, M. (2017). Developing a benchmark for emotional analysis of music. PLoS One, 12(3), e0173392. DOI: 10.1371/journal.pone.0173392 PMID: 28282400
2. Andersen, C. (2019). 10 accessible art and museum experiences for people who are blind or have low vision. Be My Eyes Blog. Retrieved from https://www.bemyeyes.com/blog/10-accessible-art-and-museum-experiences
3. Andersson, P. K., Kristensson, P., Wästlund, E., & Gustafsson, A. (2012). Let the music play or not: The influence of background music on consumer behavior. Journal of Retailing and Consumer Services, 19(6), 553–560. DOI: 10.1016/j.jretconser.2012.06.010
4. Baker, S., Centric, A., & Chennupati, S. K. (2015). Innovation in abutment-free bone-anchored hearing devices in children: Updated results and experience. International Journal of Pediatric Otorhinolaryngology, 79(10), 1667–1672. DOI: 10.1016/j.ijporl.2015.07.021 PMID: 26279245
5. Bernacki, J., Błaz˙ejczyk, I., Indyka-Piasecka, A., Kopel, M., Kukla, E., & Trawiński, B. (2016). Responsive web design: Testing usability of mobile web applications. In Nguyen, N. T., Trawiński, B., Fujita, H., & Hong, T.-P. (Eds.), Intelligent Information and Database Systems (pp. 257–269). Springer. DOI: 10.1007/978-3-662-49381-6_25
6. Chowdhury, S., & Widmer, G. (2021). On perceived emotion in expressive piano performance: Further experimental evidence for the relevance of mid-level perceptual features. arXiv preprint arXiv:2107.13231. https://arxiv.org/abs/2107.13231
7. Damnong, P., Taninpong, P., & Bootkrajang, J. (2021). Steam trap opening sound recognition. In 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 400–403).
8. Devanarayana, M. S., Chelur, M. N., Rohan, K., & Harshitha, S. (2022). Processing and recognition of pressure cooker whistle sound using MFCC and K-NN. In 2022 IEEE North Karnataka Subsection Flagship International Conference (NKCon) (pp. 1–5). DOI: 10.1109/NKCon56289.2022.10126851
9. Diamond, M. (2013). Recorder.js. Retrieved from https://github.com/mattdiamond/Recorderjs (Accessed: 2024-07-31).
10. Florian, H., Mocanu, A., Vlasin, C., Machado, J., Carvalho, V., Soares, F., Astilean, A., & Avram, C. (2017). Deaf people feeling music rhythm by using a sensing and actuating device. Sensors and Actuators. A, Physical, 267, 431–442. DOI: 10.1016/j.sna.2017.10.034
11. Golec, M., Gill, S. S., Cuadrado, F., Parlikad, A. K., Xu, M., Wu, H., & Uhlig, S. (2023). Atom: AI-powered sustainable resource management for serverless edge computing environments. IEEE Transactions on Sustainable Computing.
12. Golec, M., Gill, S. S., Golec, M., Xu, M., Ghosh, S. K., Kanhere, S. S., Rana, O., & Uhlig, S. (2023). BlockFaaS: Blockchain-enabled serverless computing framework for AI-driven IoT healthcare applications. Journal of Grid Computing, 21(4), 63. DOI: 10.1007/s10723-023-09691-w
13. Golec, M., Golec, M., Xu, M., Wu, H., Gill, S. S., & Uhlig, S. (2025). PRICELESS: Privacy enhanced AI-driven scalable framework for IoT applications in serverless edge computing environments. Internet Technology Letters, 8(1), e510. DOI: 10.1002/itl2.510
14. Good, A., Reed, M. J., & Russo, F. A. (2014). Compensatory plasticity in the deaf brain: Effects on perception of music. Brain Sciences, 4(4), 560–574. DOI: 10.3390/brainsci4040560 PMID: 25354235
15. Golec, M., Ozturac, R., Pooranian, Z., Gill, S. S., & Buyya, R. (2021). IFaaSBus: A security-and privacy-based lightweight framework for serverless computing using IoT and machine learning. IEEE Transactions on Industrial Informatics, 18(5), 3522–3529. DOI: 10.1109/TII.2021.3095466
16. Golec, M., Walia, G. K., Kumar, M., Cuadrado, F., Gill, S. S., & Uhlig, S. (2024). Cold start latency in serverless computing: A systematic review, taxonomy, and future directions. ACM Computing Surveys, 57(3), 1–36. DOI: 10.1145/3700875
17. Golec, M., Wu, H., Ozturac, R., Parlikad, A. K., Cuadrado, F., Gill, S. S., & Uhlig, S. (2024). Captain: A testbed for co-simulation of scalable serverless computing environments for AIoT enabled predictive maintenance in Industry 4.0. IEEE Internet of Things Journal, 1. DOI: 10.1109/JIOT.2024.3488283
18. Hafiz, N. F., Mashohor, S., Shazril, M. H. S. E. M. A., Rasid, M. F. A., & Ali, A. (2023). Comparison of mel frequency cepstral coefficient (MFCC) and mel spectrogram techniques to classify industrial machine sound. In 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) (pp. 273–278). DOI: 10.1109/SKIMA59232.2023.10387339
19. Ho-Ching, F., McGrenere, J. S., & Graf, P. M. (2003). Designing for individuals with memory impairments. University of British Columbia, Tech. Rep. Retrieved from https://www.cs.cmu.edu/~io/publications/old-pubs/469-ho-ching.pdf
20. International Agency for the Prevention of Blindness. (2020). Global estimates of vision loss. Retrieved from https://www.iapb.org/learn/vision-atlas
21. Iversen, J. R., Patel, A. D., Nicodemus, B., & Emmorey, K. (2015). Synchronization to auditory and visual rhythms in hearing and deaf individuals. Cognition, 134, 232–244. DOI: 10.1016/j.cognition.2014.10.018 PMID: 25460395
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