Piano Automatic Computer Composition by Deep Learning and Blockchain Technology

Huizi Li

doi:10.1109/access.2020.3031155

Abstract

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.

Highlights

Due to the rapid economic development and the continuous improvement of living standards, the demand for the public for spiritual culture continue to increase, accompanied by the improvement of appreciation
Based on the excellent characteristics of deep learning methods and blockchain technology, the Recurrent Neural Network (RNN) model and Gated Recurrent Unit (GRU) network based on deep learning are applied to the piano automatic computer composition neural network model, and the GRU-RNN model is proposed
After the blockchain technology is integrated into its copyright protection and management, it has been found that compared with other music composition generation methods, the GRU-RNN model has shown satisfactory effects in manual analysis and evaluation, as well as in the pause analysis of music passages

Summary

Introduction

Due to the rapid economic development and the continuous improvement of living standards, the demand for the public for spiritual culture continue to increase, accompanied by the improvement of appreciation. Music art can express emotions and resonate, and it has played an influential role in the past, especially in contemporary society [1], [2]. In the field of music and art, piano has a wealth of musical theory expression ability, which is a key category in this field [3], [4]. In the generation of piano music, manual music composition puts high demands on professional knowledge, such as precise mastery of music theory and harmony. A person with complete music knowledge reserve can create qualified music scores. For the average participating users, the lack of professionalism makes it harder for them to complete

Methods

Findings

Discussion

Conclusion