Abstract
The purpose is to realize the intelligent reform of piano online teaching and the intelligent optimization of wireless networks. Empirical research is realized with quantitative research and algorithm simulation as the starting point. First, regression fitting algorithm and Relief F weight algorithm are adopted to extract the effectiveness of each characteristic variable. Next, under the guidance of metric learning theory, K-Nearest Neighbors (KNN) in Projected Feature Space (P-KNN) algorithm is proposed to complete the hierarchical recognition of piano teaching influence features. Metric Learning With Support Vector Machine (ML-SVM) classification algorithm is employed to identify the feature performance affecting piano teaching. Finally, the performance of P-KNN algorithm and ML-SVM algorithm is compared with KNN algorithm and Information-Theoretic-Metric-Learning (ITML) algorithm. It is concluded that the recognition accuracies of P-KNN and ML-SVM are 82.78% and 83.97%, respectively. Based on the quantitative research on the characteristics affecting piano teaching, artificial intelligence and wireless network optimization are combined to explore the implementation path of intelligent technology in piano teaching reform, reflect the use value of modern science and technology in piano teaching, and innovate the process of music online education reform of piano teaching.
Highlights
Art education is a crucial part of China’s education
Through the analysis of algorithm distribution state of teaching difficulties and feature performance, K-Nearest Neighbors (KNN) nonlinear classification algorithm is improved based on the original theory to avoid the defect that it cannot make full use of the data obtained in training to identify knowledge
The results show that the algorithm effectively improves the classification performance of multiclassification Lagrangian Support Vector Machine (LSVM) based on Gaussian radial basis function (RBF) kernel, while the classification effect of multiclassification SVM based on linear kernel function is not as good as logistic regression (LR)
Summary
Art education is a crucial part of China’s education. Piano teaching reform is urgent with the infiltration of artificial intelligence (AI) in the art education system [1,2,3]. Realizing intellectualization in the process of piano online teaching, implanting AI system [4, 5], and applying network technology to transform and expand the structure of original musical instruments can better serve teaching and life without changing the traditional piano and improve the popularity and influence of piano art in social life [6, 7]. From the perspective of the clustering algorithm, some scholars have verified the function distribution state between teaching difficulty and characteristic performance [10,11,12] but ignored the reference value of existing teaching problems in the research process. The main and urgent problems to be solved in the piano teaching reform based on AI are to expand the characteristic variables affecting piano teaching and extract them by algorithm under the guidance of AI to make up for the shortcomings of human subjective recognition [13, 14]; based on each influencing characteristic variable, the classification algorithm is employed to quantify the teaching difficulty and Wireless Communications and Mobile Computing
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