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.
Highlights
Human behavior analysis has a wide application prospect and potential economic value in security monitoring, advanced human-computer interaction, video conference, behavior-based video retrieval and medical diagnosis
This paper mainly discusses the construction of piano teaching innovation model, in which we will be a small amount of behavior in the scene is defined as piano teaching behavior, and a large number of recurring The general behavior of piano teaching is defined as normal behavior
In order to reduce the computational complexity, we randomly extract a large number of samples from the first part of the behavior the samples are classified by dynamic time warping (DTW)-based spectral clustering, and the Hidden Markov Model (HMM) of the normal behavior of piano teaching can be further studied in a large sample by the iterative learning method
Summary
Human behavior analysis has a wide application prospect and potential economic value in security monitoring, advanced human-computer interaction, video conference, behavior-based video retrieval and medical diagnosis. In order to reduce the computational complexity, we randomly extract a large number of samples from the first part of the behavior the samples are classified by DTW-based spectral clustering, and the HMM of the normal behavior of piano teaching can be further studied in a large sample by the iterative learning method This method can automatically select the types and samples of the normal behavior patterns of piano teaching The HMM of the normal behavior of piano teaching is established to solve the problem of time-varying behavior, and can effectively avoid the unreliability of HMM parameters estimation in Xiang and the literature [12] .At the same time, (HMM) of iJET ‒ Vol 13, No 3, 2018. The key steps of the model are described in detail
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