Abstract
Traditional symphony performances need to obtain a large amount of data in terms of effect evaluation to ensure the authenticity and stability of the data. In the process of processing the audience evaluation data, there are problems such as large calculation dimensions and low data relevance. Based on this, this article studies the audience evaluation model of teaching quality based on the multilayer perceptron genetic neural network algorithm for the data processing link in the evaluation of the symphony performance effect. Multilayer perceptrons are combined to collect data on the audience's evaluation information; genetic neural network algorithm is used for comprehensive analysis to realize multivariate analysis and objective evaluation of all vocal data of the symphony performance process and effects according to different characteristics and expressions of the audience evaluation. Changes are analyzed and evaluated accurately. The experimental results show that the performance evaluation model of symphony performance based on the multilayer perceptron genetic neural network algorithm can be quantitatively evaluated in real time and is at least higher in accuracy than the results obtained by the mainstream evaluation method of data postprocessing with optimized iterative algorithms as the core 23.1%, its scope of application is also wider, and it has important practical significance in real-time quantitative evaluation of the effect of symphony performance.
Highlights
At present, most of the research processes of orchestral performance effect evaluation are based on the traditional “performance type and melodic characteristics research and vocal appreciation” type, supplemented by the “performance audience evaluation” research [1]
Compared with the current mainstream performance evaluation methods of data analysis and postprocessing, the symphony performance evaluation management and analysis model proposed by this research uses multitransformed neural network factors to quantitatively describe the quantitative characterization characteristics of different performance processes. e degree of agreement between the similarity of the dimensional vocal analysis model and the expected evaluation index is to complete the ranking of the influence on the performance melody with the quantitative index, which can efficiently analyze the characteristics of the factors that affect the performance of the symphony
Erefore, combining the results of Figure 9, according to the experimental results, it is possible to analyze the improvement of performance analysis and audience evaluation of one random symphony as a
Summary
Most of the research processes of orchestral performance effect evaluation are based on the traditional “performance type and melodic characteristics research and vocal appreciation” type, supplemented by the “performance audience evaluation” research [1]. E innovation of this paper is that the multilayer perceptron genetic neural network algorithm is used in the evaluation of symphony performance. On this basis, it can make full use of a large amount of symphony performance data and extract appropriate symphony performance information from it. Compared with the current mainstream performance evaluation methods of data analysis and postprocessing, the symphony performance evaluation management and analysis model proposed by this research uses multitransformed neural network factors to quantitatively describe the quantitative characterization characteristics of different performance processes. Compared with the current mainstream performance evaluation methods of data analysis and postprocessing, the symphony performance evaluation management and analysis model proposed by this research uses multitransformed neural network factors to quantitatively describe the quantitative characterization characteristics of different performance processes. e degree of agreement between the similarity of the dimensional vocal analysis model and the expected evaluation index is to complete the ranking of the influence on the performance melody with the quantitative index, which can efficiently analyze the characteristics of the factors that affect the performance of the symphony
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