Research on the practical application of BlockTune, a blockchain-based music copyright management system, in the field of music education

Abstract

Blockchain has the characteristics of decentralization and traceability, and not easy to tamper with, and has been widely used in music copyright management systems. To apply BlockTune to music education, this paper proposes a BlockTune system that utilizes blockchain and a recommendation system that utilizes deep learning. The system’s overall structure is divided into five layers, which include the application layer, control layer, consensus layer, network layer, and data layer. The functional modules are then divided into four modules, based on the FISCO BCOS alliance blockchain, using the Solidity language for smart contract writing, and using the improved PBFT consensus algorithm as the consensus algorithm of the system, which improves the consensus efficiency of the system. Finally, IPFS is used to store the backed up music works, which relieves the storage pressure on the blockchain. Through the platform loading, the time information is integrated into the neural collaborative filtering algorithm, and the music resource recommendation algorithm based on deep learning is proposed to better recommend the music that needs to be queried at this stage for the users at the right time. The BlockTune system meets the application standards in terms of the average CPU occupancy rate and maximum memory consumption. Half or more of the students think that the system can help them learn music or inspire them to create music, which shows that the system can be well applied to the field of music education and achieves the purpose of the system design in this paper.

Keywords

• blockchain
• deep learning
• copyright management
• music resource recommendation
• music education
• 97m80