Semantic Web 3.0 Streaming-Based Music Application

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Accelerating Discoveries in Data Science and Artificial Intelligence I (ICDSAI 2023)

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.

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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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

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