The application of HMM algorithm based music note feature recognition teaching in universities

• Published in: Intelligent systems with applications Vol. 20; p. 200277
• Main Authors: Chen, Yunli, Zheng, Haiyang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2023; Elsevier
• Subjects: Feature recognition; Genetic algorithm; HMM algorithm; Music teaching; Musical notes; Genetic algorithm; Musical notes; HMM algorithm; Feature recognition; Music teaching
• ISSN: 2667-3053; 2667-3053
• DOI: 10.1016/j.iswa.2023.200277

•A teaching model based on Hidden Markov Model (HMM) algorithm is studied and optimized using genetic algorithm.•The HMM algorithm-based music note recognition model can improve the quality of music teaching.•The model has potential for application in the field of music teaching.•The minimum value of the objective function was about 0.739 when the variance probability and crossover probability were 0.02 and 0.6 respectively. With the development of information technology, computer technology is also gradually applied to the teaching activities of art education, and the use of multimedia technology to assist music teaching has become one of the hot research areas in universities. In order to better cultivate university students' musical exploration ability and creativity, a music note feature recognition teaching model based on Hidden Markov Model (HMM) algorithm is studied and optimized in universities by using genetic algorithm based on HMM algorithm. The music note feature recognition teaching model studied in this article combines computer multimedia technology, signal processing technology, and music theory, and uses computers to simulate the process of human cognition and analysis of music. And in this article, a music note recognition system was constructed using the features of sound level contours combined with the HMM algorithm. The data extracted during music recognition was compressed using the energy compression feature of sound level contours. At the same time, maximum likelihood estimation was used to find the optimal chord sequence, i.e., the optimal path, for the input signal. In the experimental results, the minimum value of the objective function was about 0.739 when the variance probability and crossover probability were 0.02 and 0.6, respectively. In the results, the HMM algorithm-based music note recognition model can improve the quality of music teaching and has some potential for application in the field of music teaching.