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

Authors: Fei Huang; Meiqun Liao

Addresses: College of Arts, Xiamen University, Xiamen, Fujian, 361000, China ' College of Arts, Xiamen University, Xiamen, Fujian, 361000, China

Abstract: 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.

Keywords: HMM algorithm; music teaching; feature recognition; fundamental frequency recognition method; pitch class profile; PCP.

DOI: 10.1504/IJNVO.2023.133868

International Journal of Networking and Virtual Organisations, 2023 Vol.28 No.2/3/4, pp.214 - 230

Received: 01 Sep 2022
Accepted: 14 Jan 2023
Published online: 04 Oct 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article