Application of Artificial Intelligence Technology in the Intelligent Construction of Music Education Teaching System in Colleges and Universities

Abstract

The appellation of the comprehensive framework designed to enhance the process of teaching and learning in music education is the Music Education Teaching System. In order to create an engaging and productive learning environment for students of all ages and ability levels, it blends a variety of pedagogical techniques, technology resources, and instructional strategies. In this manuscript, Application of Artificial Intelligence Technology in the Intelligent Construction of Music Education Teaching System in Colleges and Universities (AAIT-ICMES-PACDNN-EHOA) is proposed. Initially input data are gathered from Classical Music MIDI as CSV Dataset. The input data is fed to pre-processing using Pseudolinear Maximum Correntropy Kalman Filter (PMCKF) to identify missing data; then pre- processed data is fed to design the music teaching function system in Artificial Intelligence Technology utilizing PACDNN to analyse and construct the music teaching system. In generally, PACDNN doesn’t express adapting optimization strategies to determine optimal parameters to ensure Construction of Music Education Teaching System. Therefore, Elk herd Optimization Algorithm (EHOA) is to optimize PACDNN which is to perfectly Using Artificial Intelligence Technology to Construct a Music Education Teaching System. Then the proposed AAIT-ICMES-PACDNN-EHOA is implemented in Python and Analysis is done on performance parameters including ROC, F1-score, Accuracy, Precision, Specificity and Recall. Performance of the AAIT-ICMES-PACDNN-EHOA approach attains 19.27%, 23.35% and 32.60%  higher accuracy, 19.25%, 23.85% and 31.90% higher Precision and 18.87%, 22.95% and 32.99%higher Recall when analysed through existing techniques like Design of an artificial intelligence-based online interactive teaching platform for rural music education (DOITR-AI-SVM), Design of an online music education system based on artificial intelligence and multiuser detection algorithm (DOMES-AI-LDSMA), and Application of deep learning-based intelligent music signal identification and generation technology in national music teaching (IMSIG-NMT-LSTM) methods respectively.