Songs without Words: Keyboard Arrangements of Vocal Music in England, 1560–1760 by Sandra Mangsen

DeSimone, Alison

doi:10.1353/not.2018.0071

Cited by 1 publication

(1 citation statement)

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“…Vocal music, audio, and video, as well as text and many other media resources, using digital network technology after careful design so that they are interconnected with each other in a certain arrangement and combination of ways, interacting with each other to form an organic whole, to construct a new type of open-ended teaching and learning model for the current vocal music teaching to provide a powerful space-time and resource sharing. This modern teaching resource model is more capable of stimulating teachers' enthusiasm for teaching and students' interest in learning, maximizing the use of resources, and improving the efficiency of teaching and learning [6][7]. However, the use of digital network technology in vocal music teaching is not very extensive, especially for teachers engaged in vocal music teaching is still very rusty.…”

Section: Introductionmentioning

confidence: 99%

Construction of a digital library of vocal music resources based on second-order data decomposition algorithm

Wang,

Guo,

Pan

et al. 2024

Applied Mathematics and Nonlinear Sciences

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In this paper, in order to solve the modal aliasing problem of the EMD decomposition algorithm and EEMD decomposition algorithm and optimize the vocal audio data decomposition on the basis of the adaptive VMD algorithm, the EEMD-VMD second-order data decomposition algorithm is proposed to decompose the digitized time series of vocal music resources, and to update the construction of the digital library of vocal music resources. Combined with the decomposition steps of the second-order data decomposition algorithm in the vocal resources digitized library, the overall framework of the vocal resources digitized library is designed and constructed with the main line of vocal resource types and the secondary line of content categories. Design the experimental environment, preprocess the vocal audio signal features, explore the modal number K value size of the EEMD-VMD second-order data decomposition algorithm, analyze the vocal audio denoising performance of the algorithm, and compare the correlation coefficients of the IMF components of the decomposition algorithms with the original audio signals. Explore the second-order data decomposition algorithm’s emotional classification of vocal resources and the performance of vocal signal enhancement, add noise sources, and use subjective and objective evaluation methods to assess the quality of vocal PESQ and STOI after decomposition processing. The EEMD-VMD second-order data decomposition algorithm performs the most prominently in street noise environments. The enhancement results in the case of signal-to-noise ratios of -5, -10, and -15 are 3.59, respectively, 4.36, 4.29, and the mean value of the enhancement performance reaches 4.08. The second-order data decomposition algorithm’s precise processing of vocal resources provides better quality digital resources for the construction of a digital library of vocal resources.

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Section: Introductionmentioning

confidence: 99%