Model of Markov-Based Piano Note Recognition Algorithm and Piano Teaching Model Construction

Abstract

Piano note recognition is a process that converts music audio files into digital music files automatically, which is critical for piano assistant training and automatic recording of musical pieces. The Merle spectral coefficients, for example, have been used to implement the majority of the existing examples. The piano is one of the most popular forms of student education in today’s world. Piano teachers should be aware of the implications. We can only truly adapt piano teaching to the educational purposes of higher education institutions if we implement a systematic, progressive, practical, and innovative philosophy of piano teaching. The Markov model is a statistical model that is widely used in speech signal processing. This thesis develops a set of mathematical models for piano speech recognition based on the Markov model, learns them systematically and scientifically, and achieves a better teaching effect. It is demonstrated that the Markov method detects the corresponding endpoints with an accuracy of 72.83 percent, which is 16.42 percent better than the a priori method. In terms of amplitude and phase, the Markov model shows a significant improvement. The findings of this study can be used to improve piano playing techniques taught to students in accordance with their favourite popular music, depending on the theme.