Abstract
Artists have been facing many challenges in the present music industry. Royalties refer to getting the permission to use music from someone who has the right to control how that music is used. There’s no transparency in how these royalty distributions is calculated. This is a serious problem for the musicians; they are often the last to receive any profits, although they are the first to put in the work. Streaming services like Spotify, YouTube Music, Apple Music are a great source for young artists to experiment and assess their talents. But these platforms fall short in several important ways that are limiting an artist’s potential and the revenue that they can make. The research papers cover crypto currencies, decentralized applications, and smart contracts in first and second generations. In third generation, apps are linked to autonomous societies which increased complicated settings in decentralization, and this even delivered information about the healthcare. In our project, we build a Next.js application with a Solana backend, using Anchor Framework. Users will first be required to log in through their Phantom Wallets. Once the wallet connection is established, they must pay 0.1 sols to be able to access the website. Once the transaction is successfully completed, the users of the application may listen to music, make playlists, and follow artists. An artist can upload their music through the upload music option present on the website. Here they enter the song title and the download URL or the cloud link for that song. Each time an artist uploads their work, a block of the song is created on the Solana block chain. The uploaded songs will be visible to all users who follow the artists.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
29th European Regional Conference of the International Telecommunications Society (ITS): Towards a Digital Future: Turning Technology into Markets? Trento, 1st–4th August, 2019
L.C. Arcos, The block chain technology on the music industry. Braz. J. Oper. Prod. Manag. 15(3), 439–443 (2018)
Yakovenko, A., Solana: A new architecture for a high performance block chain v 0.8.13. Whitepaper (2019)
M. O’Dair, Z. Beaven, D. Neilson, R. Osborne, P. Pacifico, Music on the Block Chain (2020)
N. Baym, L. Swartz, A. Alarcon, Sonic publics| convening technologies: Block chain and the music industry. Int. J. Commun. 13, 20 (2019)
X. Chen, X. Qu, Y. Qian, Y. Zhang, Music Recognition Using Block Chain Technology and Deep Learning. Computational Intelligence and Neuro science, vol 2022 (2022), p. 1
S. Chavan, P. Warke, S. Ghuge, R.V. Deolekar, Music Streaming Application using Block chain (2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), 2019), pp. 1035–1040
S. Yamwaja, C. Angsu Chotmetee, DMS: An Architecture of a Decentralized-based Music Streaming Platform using Block chain (2022, 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 2022), pp. 1–4
P. Behal, Listen-to-Earn: How Web3 Can Change the Music Industry (2022)
B. Saini, G. Aggarwal, A. Yadav, K. Nautiyal, A Reliable Block chain Application for Music in a Decentralized Network (In Security Analytics, 2022), pp. 1–14
S. Shaik, Pattern recognition of speech signals using Curvelet transform and artificial intelligence, in The First National Conference on Advances in Information Technology and Computing, (Sreenidhi Institute of Science & Technology., April 26–27, 2018)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Abhinav, A., Varunya, V.S., Chandra, N.U., Ramesh, G.R., Shaik, S. (2024). Semantic Web 3.0 Streaming-Based Music Application. In: Lin, F.M., Patel, A., Kesswani, N., Sambana, B. (eds) Accelerating Discoveries in Data Science and Artificial Intelligence I. ICDSAI 2023. Springer Proceedings in Mathematics & Statistics, vol 421. Springer, Cham. https://doi.org/10.1007/978-3-031-51167-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-51167-7_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-51166-0
Online ISBN: 978-3-031-51167-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)