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

iJET Volume 13, Number 3, ISSN 1863-0383 Publisher: International Association of Online Engineering, Kassel, Germany


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.


Wei, A. (2018). The Construction of Piano Teaching Innovation Model Based on Full-depth Learning. International Journal of Emerging Technologies in Learning (iJET), 13(3), 32-44. Kassel, Germany: International Association of Online Engineering. Retrieved August 15, 2018 from .