Piano Automatic Computer Composition by Deep Learning and Blockchain Technology

To explore the automatic computer composition, investigate the copyright protection and management of digital music, and expand the application of deep learning and blockchain technologies in the generation of digital music works, piano composition was taken as a sample. First, through the elaboration of the neural network methods based on deep learning, the Recurrent Neural Network (RNN), Long-Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) networks were introduced, and the deep learning-based GRU-RNN automatic composition model was constructed. Second, the blockchain technology was analyzed and expressed, and the problems in the traditional copyright protection and management of digital music were analyzed. The three aspects, i.e., ownership, right of use, and right protection, were fully considered, and the blockchain technology was integrated into the copyright protection and management of digital music. Finally, the manual analysis evaluation and pause analysis were selected as the indicators to analyze and characterize the music composition quality of the GRU-RNN model, as well as analyzing the development of the digital music market integrated with blockchain technology. The results show that the GRU-RNN model shows satisfactory effects in manual analysis evaluation or in the pause analysis of the passage. The deep learning method has great potential for application in automatic computer composition of digital music; the integration of blockchain technology has played a promotive role in the expansion and popularization of the digital music market. However, in the meantime, it still faces some technical and policy challenges. The results have a positive effect on promoting the development and application of deep learning methods and blockchain technology in digital music.