E-resources
Peer reviewed
Open access
-
Sun, Ke
Applied mathematics and nonlinear sciences, 01/2024, Volume: 9, Issue: 1Journal Article
Subjective interference is a common difficulty in vocal music teaching, and human ear audition cannot fully objectively analyze the students’ problems in vocal practice due to the influence of environment and other factors. This paper takes the convolutional neural network as the vocal music recognition algorithm and the Mel spectrum as the vocal music feature extraction algorithm and constructs the vocal music analysis model based on the optimization and improvement of the two algorithms. Then select the support vector machine, the nearest neighbor node, Wavenet, LSTM, GAN, SAGAN, CLDNN_BILSTM, and other models, as well as this paper’s model, for comparison experiments. Finally, the model was utilized in the vocal education classroom to evaluate the singing practice of four students. It is found that the MSE value of Arousal’s algorithm in this paper is the lowest, and the R values of 0.51197 and 0.71058 are the highest in the test of the MFCC vocal music feature dataset. Valence’s model in this paper has the MSE value of 0.51996, which is still the lowest, and the R² value of 0.76946, which is still the highest. This paper’s model has the best performance and results. The average rate of professional singers is 61 beats, and the model calculates the average singing rate of the four students as 77, 66, 63, and 61 beats. The first three still have a large gap compared to the standard level, and the student D level is higher. The problem of student practice analysis and vocal feature extraction and recognition in vocal teaching can be solved using new ideas and methods provided in this study.
Author
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.