Music Composition and Emotion Recognition Using Big Data Technology and Neural Network Algorithm.

Yu Wang,Bai Yuan Ding

doi:10.1155/2021/5398922

Abstract

To implement a mature music composition model for Chinese users, this paper analyzes the music composition and emotion recognition of composition content through big data technology and Neural Network (NN) algorithm. First, through a brief analysis of the current music composition style, a new Music Composition Neural Network (MCNN) structure is proposed, which adjusts the probability distribution of the Long Short-Term Memory (LSTM) generation network by constructing a reasonable Reward function. Meanwhile, the rules of music theory are used to restrict the generation of music style and realize the intelligent generation of specific style music. Afterward, the generated music composition signal is analyzed from the time-frequency domain, frequency domain, nonlinearity, and time domain. Finally, the emotion feature recognition and extraction of music composition content are realized. Experiments show that: when the iteration times of the function increase, the number of weight parameter adjustments and learning ability will increase, and thus the accuracy of the model for music composition can be greatly improved. Meanwhile, when the iteration times increases, the loss function will decrease slowly. Moreover, the music composition generated through the proposed model includes the following four aspects: sadness, joy, loneliness, and relaxation. The research results can promote music composition intellectualization and impacts traditional music composition mode.

Highlights

With rapid economic development, Chinese people are expecting a higher quality of spiritual life
Yan et al suggested a cognitive Neural Network (NN) based on Takagi Sugeno (T-S) model to train the network weights with improved Genetic Algorithm (GA). e membership function parameter adjustment strategy was combined with the momentum method and learning rate-adaptive adjustment, and the results showed that the combined method could be applied to the music recognition system, and the recognition accuracy of the algorithm was higher than other algorithms with good robustness [11]
Classical music is called Vienna classical music, which refers to the music works led by Beethoven and Mozart in the 18th century

Summary

Introduction

Chinese people are expecting a higher quality of spiritual life. Under diversified entertainment industries, music, as a kind of social ideology, can cultivate people’s sentiment, regulate their emotions, and are of great practical significance. Music can encourage new ideas and brilliant lifestyles, and it has a strong infection. Streets and shopping centers are full of music rendering, giving people passion and joy [1]. Composers have tried new methods and techniques to meet people’s needs. Under the Internet era, short-video platforms, the animation industry, and video games are attracting more and more users, in which extraordinary amounts of original music works are needed. High-quality music composition and production usually cost much time and resources, which has posed great challenges to meet users’ personalized music demands [2]

Methods

Results

Conclusion