Abstract
Music multimedia is one of the more popular types of digital music. This article is based on the hidden Markov model (HMM) and proposed this kind of music multimedia automatic classification method. The method not only analyzes the characteristics of traditional music in detail but also fully considers the important characteristics of other music. At the same time, it uses bagging to train two groups of HMMs and automatically classifies them to achieve a better classification effect. This paper optimizes the variable parameters from different aspects such as model structure, data form, and model change to obtain the optimal HMM parameter value. This method not only considers the prior knowledge of feature words, word frequency, and number of documents but also fuses the meaning of the feature words into the hidden Markov classification model. Finally, by testing the hidden Markov model used in this paper on the music multimedia data set, the experimental results show that the method in this paper can effectively perform automatic classification according to the melody characteristics of music multimedia.
Highlights
With the continuous development of Internet technology and the improvement of the technological level, different Internet-based music multimedia began to emerge
How to obtain the specific content source from the initial music multimedia data, lacking the definition of the music content, has become a huge challenge for the current automatic classification of music multimedia because the music multimedia signal belongs to a way of time sequence, which can be concealed according to its concealment
The hidden Markov model is applied to the automatic classification process of music multimedia. is method can be based on the characteristics of the automatic lyrics of the music multimedia, and the lyrics, word frequency, content, and meaning of the lyrics of the music multimedia are used as the current prior knowledge
Summary
With the continuous development of Internet technology and the improvement of the technological level, different Internet-based music multimedia began to emerge. The hidden Markov model is applied to the automatic classification process of music multimedia. OT, and the transition probability between each state or between the states is Advances in Multimedia represented by a0, where each observation sequence is used as each frame MFCC parameters. In this model, the observation sequence o1, o2, . Ξt(i, j) represents the given training sequence O and the model parameter λ, the probability that the Markov time t +. Δt(i) represents the probability of accumulating the output value of the ith state at time t, ψt(i) represents the continuous state parameter of the ith state at time t, q∗t is the state at time t in the optimal state sequence, and P∗ is the final output probability
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