Teaching Integration of Piano and Traditional Music Elements in Colleges and Universities Based on Network Flow Optimization

Abstract

In this paper, from the basis of piano music theory, pitch and twelve equal temperaments are studied, and due to the characteristics of the piano’s construction, there are different timbres and harmonic structures. In order to realize the digitization of music signals for piano teaching, the collected analog signals are converted into digital signals by Fourier transform, the samples are inputted into the neural network model to get the output results, and the loss function based on Markov’s music language model in piano teaching is used to find the partial derivatives of the parameters to realize the maximization of the efficiency of the piano pitch and note recognition. According to the multi-task learning in neural networks and the envelope curve of notes, we construct the automatic music transcription model in piano teaching based on CNN-HMM and use the simulation experiment analysis to empirically analyze the piano teaching based on CNNHMM. The results show that the dual-channel audio results are improved by 2.68% in F1 value, 3.18% in accuracy and 2.61% in recall than the mono audio results. That is, dual-channel audio has richer information than mono audio, which enables the CNN-HNN network to learn richer features and thus improve the pitch and note recognition results. In this paper, the reliability of the teaching quality evaluation results of the CNN-HMM-based quantitative assessment model for facilitating the combination of electronic organ and piano teaching concepts ranges from 78.1% to 85.4%. This study provides guiding value for the development of digital piano teaching, which is of great significance for the innovative reform of piano teaching in colleges and universities.

Keywords

• convolutional neural network
• markov algorithm
• envelope curve
• harmonic structure
• piano teaching
• 97c70