An analytical study of folk musical instruments of the rural area of Tripura

Main Article Content

Joyanta Sarkar

Abstract

A music library or music application's listening and search experiences can be greatly enhanced by music recommendation systems. There is simply too much music available in the market for a user to efficiently navigate tens of millions of songs. The demand for good music recommendations is so great that the field of MRS (Music Recommendation Systems) is growing quickly. Extraction of relevant information from user reviews of instrumental music was the primary motivation behind the development of the rating-based recommendation system. On the passage of times most of instruments are not being used by the new generation people as a result, these musical instruments are being wiped out from the world. The research discusses the main features of the performance of Folk Instrumental Music (FIM). In public concert Tripura conditions, it addresses different components of the FIM conducted. The research also examines the components historically recommended by FIM performers and the results provided by music in performances such as raga mood, tranquilly, scalability and astonishments.

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How to Cite
Sarkar, J. (2023). An analytical study of folk musical instruments of the rural area of Tripura. Research Journal in Advanced Humanities, 4(2). https://doi.org/10.58256/rjah.v4i2.1064
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Articles

How to Cite

Sarkar, J. (2023). An analytical study of folk musical instruments of the rural area of Tripura. Research Journal in Advanced Humanities, 4(2). https://doi.org/10.58256/rjah.v4i2.1064

References

A. Niyazov, E. Mikhailova and O. Egorova, "Content-based Music Recommendation System," 2021 29th Conference of Open Innovations Association (FRUCT), Tampere, Finland, pp. 274-279, 2021.

F.L. Schiavoni, and L. Costalonga, “Ubiquitous music: A computer science approach,” Journal of Cases on Information Technology, Vol. 17, No. 4, pp. 20-28,2015.

E. R. Miranda, E. Braund, and S. Ventakesh, “Composing with bioimemristors”, Computer Music Journal, Vol. 42, No. 3, pp 28- 46, 2018.

R.B. Shapiro, A. Kelly, M. Ahrens, B. Johnson, H. Politi, and R. Fiebrink, “Tangible distributed computer music for youth,” Computer Music Journal, Vol. 41, No. 2, pp. 52-68, 2017.

W. Dai, K. Yu, “Contestability in the digital music player market”, Journal of Industry, Competition and Trade, Vol 19, pp 293-311, 2019.

G. Waddell, and A. Williamon, “Technology use and attitudes in music learning”, Technology Enhanced Music Learning and Performance, Vol. 11, No. 9, pp 80-95, 2019.

D. Wang, X. Zhang, Y. Wan, D. Yu, G. Xu and S. Deng, "Modeling Sequential Listening Behaviors With Attentive Temporal Point Process for Next and Next New Music Recommendation," in IEEE Transactions on Multimedia, vol. 24, pp. 4170-4182, 2022, doi: 10.1109/TMM.2021.3114545.

H. -G. Kim, G. Y. Kim and J. Y. Kim, "Music Recommendation System Using Human Activity Recognition From Accelerometer Data," in IEEE Transactions on Consumer Electronics, vol. 65, no. 3, pp. 349-358, Aug. 2019, doi: 10.1109/TCE.2019.2924177.

D. Ayata, Y. Yaslan and M. E. Kamasak, "Emotion Based Music Recommendation System Using Wearable Physiological Sensors," in IEEE Transactions on Consumer Electronics, vol. 64, no. 2, pp. 196-203, May 2018, doi: 10.1109/TCE.2018.2844736.

D. Wang, X. Zhang, D. Yu, G. Xu and S. Deng, "CAME: Content- and Context-Aware Music Embedding for Recommendation," in IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 3, pp. 1375-1388, March 2021, doi: 10.1109/TNNLS.2020.2984665