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

Authors

  • An Shi Wei

DOI:

https://doi.org/10.3991/ijet.v13i03.8369

Abstract


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.

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Published

2018-03-05

How to Cite

Wei, A. S. (2018). The Construction of Piano Teaching Innovation Model Based on Full-depth Learning. International Journal of Emerging Technologies in Learning (iJET), 13(03), pp. 32–44. https://doi.org/10.3991/ijet.v13i03.8369

Issue

Vol. 13 No. 03 (2018)

Section

Papers

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