Popular Song Composition Based on Deep Learning and Neural Network

Abstract

For the general public, composition appears to be professional and the threshold is relatively high. However, automatic composition can improve this problem, allowing more ordinary people to participate in the composition, especially popular music composition, so the music becomes more entertaining, and its randomness can also inspire professionals. This article combines deep learning to extract note features from the demonstration audio and builds a neural network model to complete the composition of popular music. The main work of this paper is as follows. First, we extract the characteristic notes, draw on the design process of mel-frequency cepstral coefficient extraction, and combine the characteristics of piano music signals to extract the note characteristics of the demonstration music. Then, the neural network model is constructed, using the memory function of the cyclic neural network and the characteristics of processing sequence data, the piano notes are combined into a sequence according to the musical theory rules, and the neural network model automatically learns this rule and then generates the note sequence. Finally, the ideal popular piano music scores are divided into online music lover scores and offline professional ratings. The score index is obtained, and each index is weighted by the entropy weight method.