Analysis of the application of HMM algorithm in teaching musical note feature recognition in universities

With the rapid development of music education and information technology in colleges and universities, how to improve the efficiency of teachers' teaching in current music courses has increasingly become a focus of public attention. This study aims to propose an HMM algorithm based on the application of music note feature recognition teaching in colleges and universities. The experimental results show that the HMM algorithm is used in the music frequency sample signal after pre-processing, and its target accuracy is reached after 20 training sessions. Comparing the HMM algorithm with the other two algorithms, the results show that its correct rate is about 99.56%, and the probability of occurrence of insertion error and elimination error is 0.52% and 2.58%, which is better than the other two algorithms. In summary, it shows that the research proposed HMM algorithm has some practical value and relevance to the teaching of music in colleges and universities.