Lifelong Music Learning for Sustainable Development

Oh, Yonghyup; Roels, Johanna Maria; Petegem, Peter Van

doi:10.1007/978-3-319-63534-7_19

Cited by 1 publication

(2 citation statements)

References 9 publications

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“…Let the ideal output of the 1st training sample be , which is defined as: (8) Where describes the coordination threshold. Then the total error of all sample outputs is defined as: (9) For the initial values of the randomly generated weight coefficients, there must be an error between the actual output and the desired output.…”

Section: Ga-bp Network Modelingmentioning

confidence: 99%

“…However, the fundamental reason for the current crisis of traditional vocal music lies in the changing ecological environment and the "imbalance" in the development of Chinese vocal music culture [5][6][7]. Therefore, the conservation and development of traditional vocal music must not deviate from traditional vocal music and its related ecosystem, and must not deviate from the direction of development recognized by contemporary human society, which is the path of "sustainable development" [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Sustainable development of vocal music ecology in a digital ecological environment

Liu

2023

3C TIC

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The phenomenon of noise interference during vocal transmission can lead to the problem of poor vocal transmission quality. This paper proposes a study on the sustainability of vocal ecology in a digital ecological environment. First, the matching tracking algorithm can extract the time-frequency characteristics of the effective signal, attenuate the interference of noise, and improve the propagation quality. A sustainable development GA-BP network model is established, and the adjustment amount of each weighting coefficient is obtained according to the gradient algorithm and using the inertia adjustment strategy. The coordination is regulated through feedback control strategies to ultimately achieve ecological sustainability of vocal music. The analysis results show that the average relative error of the simulation prediction of the sustainable development GA-BP network model is 3.54%, the maximum relative error is 8.11%, and the average relative error is within 5% of 70%. It has significant superiority and high efficiency in comparison with the prediction degree of the traditional model.

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Section: Ga-bp Network Modelingmentioning

confidence: 99%