The Construction of Piano Teaching Innovation Model Based on Full-depth Learning

• Main Author: An Shi Wei
• Format: Article
• Language: English
• Published: Kassel University Press 2018-03-01
• Series: International Journal of Emerging Technologies in Learning (iJET)
• Online Access: http://online-journals.org/index.php/i-jet/article/view/8369

This paper presents a new method of building piano teaching innovation model based on full depth learning. The model includes the following main steps: (1) The normal behavior samples of piano teaching are obtained by the method of spectral clustering based on dynamic time homing (DTW), and the hidden Markov model; (2) to further train the hidden Markov model parameters in a large sample by means of iterative learning; (3) to use the maximum a posteriori (MAP) adaptive method to estimate the Hidden Markov Model (HMM) of the piano teaching behavior in a supervised manner; (4) The behavioral hidden Markov topology model is established for model estimation. The main features of this method are: it can automatically select the kinds and samples of the normal behavior patterns of piano teaching to establish an innovative model of piano teaching; the problem of under-learning of Hidden Markov Model (HMM) can be avoided in the case of fewer samples. The experimental results show that this model is more reliable than other methods.