Abstract
The current music teaching can effectively improve students' music emotional expression indirectly. How to use the PSO-BP neural network to realize the quantitative research of music emotional expression is the current development trend. Based on this, this paper studies the influence factors of music emotion expression based on PSO-BP neural network and big data analysis. Firstly, a music emotion expression analysis model based on PSO-BP neural network algorithm is proposed. The autocorrelation function is used to simulate the emotion expression information in music. Through the maximum value of the autocorrelation function curve in the detection process, the vocal music signal is restored, and then the emotion expressed is analyzed. Secondly, the influence factors of PSO-BP neural network algorithm in music emotion expression are analyzed. The improved PSO-BP neural network algorithm and multidimensional data model are used for comprehensive analysis to accurately analyze the emotion in music expression, and the fuzzy evaluation method and analytic hierarchy process are used for quality evaluation. Finally, the validity of the music emotion analysis model is verified by many experiments.
Highlights
Computational Intelligence and Neuroscience multidimensional quantification of different types of music sentiment analysis processes and realize the construction of regular functions according to their differences [12]
Samek et al have verified the effectiveness of the music emotion evaluation system in the PSO-BP neural network through practice, and the results show that the innovative PSO-BP neural network analysis method can effectively perform high accuracy on different types of databases [14]
In order to further study the influence of audio signal on music emotion expression under PSO-BP neural network, this study evaluates the simulation results of influencing factors of human music emotion expression through PSO-BP neural network algorithm. is method is a multicriteria decision analysis method which combines qualitative and quantitative analysis
Summary
Received 27 July 2021; Revised 18 August 2021; Accepted 20 August 2021; Published 6 September 2021. In the research of processing signals and performing sentiment analysis, scholars have confirmed that the neural network algorithms can efficiently and accurately complete the feature extraction and internal correlation analysis of data information and has formed a neural network algorithm [16,17,18,19] It is the core data analysis system and evaluation standard, but there are few breakthroughs and advances in the analysis of emotional uniqueness of music signals and the adaptive discrimination of emotional types [20, 21]. Erefore, research scholars have done a lot of basic research on the expression of music emotion, the research results are relatively few [22] Under this background, this paper puts forward the influence analysis model of music emotion based on improved PSO-BP neural network algorithm. E PSO-BP neural network algorithm is a backpropagation neural network algorithm optimized with the characteristics of particle swarms. e algorithm combines the BP drop to adjust the weights, through the revised analysis of the network weights and thresholds according to the idea of particle swarm optimization, data training, and deep learning in the BP neural network. is is because the particle swarm can roughly search the data on a global scale to obtain an initial solution, which reduces the difficulty of
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