Automatic generation of music elements based on artificial intelligence algorithms

Abstract

With the rapid development of computer information technology in China, algorithmic composition technology has received more attention in the field of musical composition. However, the accuracy of existing methods based on artificial intelligence algorithms for composition is relatively low, and the production effect cannot meet practical requirements when facing complex tracks. In view of this, this research designed the music element automatic generation method based on recurrent neural network. A music element automatic generation model based on resonant neural network is proposed. The improved algorithm is experimentally validated. The experiment showed that the system combined with the average field connection network, initial universal connection of resonant neural network, and detuned oscillator performed the best. The F-value reached 77.2%. The chord generation accuracy of the LSTM-RNN model was 81.99%, 81.65%, 81.02%, and 80.47%, respectively. The designed method can effectively achieve music production, meet high precision design requirements, and achieve good design results. This indicates that the music element generation method based on recurrent gradient frequency proposed in the study has good performance. It can accurately generate music elements, providing certain assistance and reference for the development of automatic generation technology of music elements in China. It is recommended to apply this method to more diverse scenarios in the future to complete music element generation.