Solfeggio Teaching Method Based on MIDI Technology in the Background of Digital Music Teaching

Abstract

This research aims at teaching solfeggio and ear training in college music and proposes a teaching method for college music note recognition that combines the musical instrument digital interface (MIDI) and hidden Markov models (HMM). The experiment showcases that after preprocessing the music frequency sample signal using HMM model, it achieves the target accuracy after 20 times of training. From the HMM transition probability matrix diagram estimated from all training data sets, it can be seen that the transition matrix is close to the diagonal matrix. This indicates its high transfer efficiency. This study compares the HMM model with the other two algorithms, and the results show that its accuracy rate is about 99.56%. The probability of insertion errors and elimination errors is 0.52% and 2.58%. This is superior to the other two algorithms. In summary, the HMM model proposed in the study has extremely strong performance in the teaching of music note feature recognition in universities and can provide better teaching methods.