Vocal Music Teaching Pharyngeal Training Method Based on Audio Extraction by Big Data Analysis

Huang, Chunyuan

doi:10.1155/2022/4572904

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…The artistic expression of the voice in opera, stage, film, television, and radio is referred to as artistic voice in the industry (Huang, 2022). There are diversities of styles and evaluation standards, but good voices with many popular styles must have something in common, and this commonality can be extracted as a popular evaluation standard for good voices.…”

Section: Introductionmentioning

confidence: 99%

A Method for Improving the Pronunciation Quality of Vocal Music Students Based on Big Data Technology

Shen,

Zhao

2023

International Journal of Web-Based Learning and Teaching Technologies

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With the development of internet technology, big data has been used to evaluate the singing and pronunciation quality of vocal students. However, current methods have several problems such as poor information fusion efficiency, low algorithm robustness, and low recognition accuracy under low signal-to-noise ratio. To address these issues, this article proposes a new method for evaluating sound quality based on one-dimensional convolutional neural networks. It uses sound preprocessing, BP neural networks, wavelet neural networks, and one-dimensional CNNs to improve pronunciation quality. The proposed 1D CNN network is more suitable for one-dimensional sound signals and can effectively solve problems such as feature information fusion, pitch period detection, and network construction. It can evaluate singing art sound quality with minimum errors, good robustness, and strong portability. This method can be used for the evaluation and diagnosis of voice diseases, helping to improve students' professional abilities.

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Section: Introductionmentioning

confidence: 99%

A Method for Improving the Pronunciation Quality of Vocal Music Students Based on Big Data Technology

Shen,

Zhao

2023

International Journal of Web-Based Learning and Teaching Technologies

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The Application of Spectrogram in the Teaching of High-level Vocal Music Major Students

Fang

2024

Applied Mathematics and Nonlinear Sciences

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Vocal music teaching has developed to the present day, still following the method of oral transmission, but with the development of modern science and technology, sound images can be processed to clearly see the quality of sound used. Students’ vocal signals are used in the article to extract articulation features and construct a vocal spectrogram. The vocal spectrogram is used in vocal music teaching to enhance the students’ timbre resonance. In order to verify its application in dissecting students’ singing effects, the state of different students’ American soprano articulation area is concretized on the spectrogram in terms of students’ voice change differences, breathing aspects, and vocal resonance while comparing the differences between the manual evaluation and the evaluation of the vocal spectrogram analysis. It can be seen that the minimum fundamental frequency of student A in the first group is 191.7 lower than that of student B, which is 287.8, and the maximum fundamental frequency of 1033.2 is also significantly higher than that of student B, which is just the same as the conclusion drawn from the manual scale. To sum up, according to the sound spectrogram, the teacher can well analyze the students’ voice waveforms visually, and the students can adjust the vocal state through the voice waveforms so as to carry out the correct reinforcement training and add new means for the traditional mode of vocal teaching, so as to make the teaching of vocal music gradually realize the visualization and intuition.

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Fuzzy logical system for personalized vocal music instruction and psychological awareness in colleges using big data

Wang

2024

IFS

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Traditional psychological awareness relating to vocal musical instruction often disregards the impact of earlier experiences on music learning could result in a gap in meeting the needs of individual students. Conventional learning techniques of music related to psychological awareness for each individual has been focused on and addressed in this research. Technological upgrades in Fuzzy Logic (FL) and Big Data (BD) related to Artificial Intelligence (AI) are provided as a solution for the existing challenges and provide enhancement in personalized music education. The combined approach of BD-assisted Radial Basis Function is added with the Takagi Sugeno (RBF-TS) inference system, able to give personalized vocal music instruction recommendations and indulge psychological awareness among students. Applying Mel-Frequency Cepstral Coefficients (MFCC) is beneficial in capturing variant vocal characteristics as a feature extraction technique. The BD-assisted RBF can identify the accuracy of pitch differences and quality of tone, understand choices from students, and stimulate psychological awareness. The uncertainties are addressed by using the TS fuzzy inference system and delivering personalized vocal training depending on different student preference factors. With the use of multimodal data, the proposed RBF-TS approach can establish a fuzzy rule base in accordance with the personalized emotional elements, enhancing self-awareness and psychological well-being. Validation of the proposed approach using an Instruction Resource Utilization Rate (IRUR) gives significant improvements in engaging students, analyzing the pitching accuracy, frequency distribution of vocal music instruction, and loss function called Mean Square Error(MSE). The proposed research algorithm pioneers a novel solution using advanced AI algorithms addressing the research challenges in existing personalized vocal music education. It promises better student outcomes in the field of music education.

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Vocal Music Teaching Pharyngeal Training Method Based on Audio Extraction by Big Data Analysis

Cited by 3 publications

References 13 publications

A Method for Improving the Pronunciation Quality of Vocal Music Students Based on Big Data Technology

A Method for Improving the Pronunciation Quality of Vocal Music Students Based on Big Data Technology

The Application of Spectrogram in the Teaching of High-level Vocal Music Major Students

Fuzzy logical system for personalized vocal music instruction and psychological awareness in colleges using big data

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