Abstract
Piano performance is an art with rich artistic elements and unpredictable performance skills. It is an important carrier for playing beautiful piano sounds. The generation of musical tension and expression of piano performance is a vivid display of piano performance skills. In piano performance, we should pay attention to the cultivation and flexible application of performance skills. In order to ensure the richness and artistry of piano performance, it is fully based on the artistic characteristics of piano performance. Through in-depth analysis of the principle of the hidden Markov model, it is applied to the multimedia recognition process of piano playing music. In the process of obtaining the template, the fundamental frequency of the piano playing music differs greatly, and the piano playing music appears during the performance process. For the problem of low recognition rate, this paper proposes a multimedia recognition method for piano music. Finally, the analysis of experimental results shows that the method proposed in this paper has a 16% higher recognition rate than the traditional method, and it has a certain value in the multimedia recognition of piano music.
Highlights
With the continuous development of science and technology and electronic products, piano music recognition technology has been further developed. e piano performance recognition technology has been transformed from the indoor research process to the market application stage and has developed to a relatively high level [1,2,3]
Due to the large differences between the characteristics of piano music and human piano music and the previous piano music recognition technology cannot meet the needs of piano performance music, the research on piano performance recognition technology has become the focus of research by scholars at home and abroad. is paper proposes a multimedia recognition system for piano performance music based on the hidden Markov model. rough continuous improvement of piano performance music recognition ability, it can be widely used in the field of piano performance music recognition
A hidden Markov model with four states S1∼S4 is used for computational processing. e input observation sequence is represented by o1, o2, . . . , oT, and the transition probability between each state or between the states is represented by a0, where each observation sequence is used as each observation sequence
Summary
With the continuous development of science and technology and electronic products, piano music recognition technology has been further developed. e piano performance recognition technology has been transformed from the indoor research process to the market application stage and has developed to a relatively high level [1,2,3]. When using P (O|λ) to reach the maximum value, since the training sequence of each experiment is limited, the best method of estimating parameters cannot be realized. In this case, the Bam–Welch algorithm uses P (O|λ) with a recursive idea; the part is very large, and the model parameter λ (A, B, π) is obtained. Ξt(i, j) represents the given training sequence O and the model parameter λ, the Markov chain at time t is in the θi state and the probability of the θi state at time t + 1, and Tt −11ξt(i) represents the expected value of the number of transitions from the state θt to the state θi. 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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