Optimization of Music Teaching Management System for College Students Based on Similarity Distribution Method

Lian, Jue

doi:10.1155/2022/6444554

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…The distinctive qualities of neural networks adaptive learning, nonlinear processing and high fault tolerance have led to their widespread use in a wide range of assessment problems. Additionally, Neural Network provides clear benefits in tackling the problem of assessment at the instructional level [18]. This study builds on this basis by examining the basic theories and practices of traditional instructional assessment, along with its features and contemporary issues.…”

Section: Introductionmentioning

confidence: 99%

Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)

Kegang Lu

2024

jes

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Colleges and universities play a crucial role in nurturing talent and providing highly skilled individuals for various sectors of society. Through modifications over time, the model of music education at colleges and universities has advanced. However, there are still numerous issues that demand careful consideration. This manuscript proposes a hierarchically gated recurrent neural network (HGRNN) optimized with the lyrebird Optimization Algorithm (LOA) for predicting music teaching mode of colleges and universities (MTM-HGRNN-LOA). Initially, the data is collected via real time basis. Afterward, the data is fed to an unscented trainable kalman filter (UTKF) based pre-processing process. In the pre-processing segment, it enhances training rate and eliminates the of batch size dependency. The pre-processing output is given to modified spline-kernelled chirp let transform (MSKCT). The input signal undergoes feature extraction to derive the primary features, which are subsequently combined to yield more comprehensive features in an efficient manner. After that, the extracted features are given to a hierarchically gated recurrent neural network and lyrebird optimization algorithm for effectively classifying the music teaching mode for best, good, normal, satisfactory and poor. The weight parameters of hierarchically gated recurrent neural network are optimized using the lyrebird optimization algorithm. The proposed method is implemented in python and evaluated their performance with existing methods. The performance metrics, like precision, F1-score, accuracy, specificity, sensitivity, and ROC is analysed to the proposed method's performance. The proposed MTM-HGRNN-LOA methods of accuracy are provide 97% best, 98% good, 95% normal, 98% satisfactory and 97% poor music teaching mode. The existing methods MTM-CNN, MTM-BPNN and MTM-GNN, the specificity becomes 90%, 70%, 79% best, 77%, 75%, 65% good, 66%, 85%, 84% normal, 59%, 58%, 70% satisfactory, 61%, 79%, 81% poor music teaching mode. The results show that the proposed MTM-HGRNN-LOA method outperforms other existing techniques, such as online vocal music teaching quality using Back Propagation neural network and convolutional neural network based College-Level Music Teaching Quality Evaluation.

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Section: Introductionmentioning

confidence: 99%

Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)

Kegang Lu

2024

jes

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“…Therefore, an automatic sentiment analysis system is required, which usually contains sentiment classification modules. According to the different symbols, sentiment analysis can be divided into text sentiment analysis [ 6 ], video sentiment analysis [ 7 ], and audio sentiment analysis [ 8 ]. In the research of emotion recognition of audio, it mainly deals with the audio signal.…”

Section: Introductionmentioning

confidence: 99%

Design of Semantic Matching Model of Folk Music in Occupational Therapy Based on Audio Emotion Analysis

Ouyang

2022

Occupational Therapy International

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The main semantic symbol systems for people to express their emotions include natural language and music. The analysis and establishment of semantic association between language and music is helpful to provide more accurate retrieval and recommendation services for text and music. Existing researches mainly focus on the surface symbolic features and association of natural language and music, which limits the performance and interpretability of applications based on semantic association of natural language and music. Emotion is the main meaning of music expression, and the semantic range of text expression includes emotion. In this paper, the semantic features of music are extracted from audio features, and the semantic matching model of audio emotion analysis is constructed to analyze ethnic music audio emotion through feature extraction ability of deep structure. The model is based on the framework of emotional semantic matching technology and realizes the emotional semantic matching of music fragments and words through semantic emotional recognition algorithm. Multiple experiments show that when W = 0.65 , the recognition rate of multichannel fusion model is 88.42%, and the model can reasonably realize audio emotion analysis. When the spatial dimension of music data changes, the classification accuracy reaches the highest when the spatial dimension is 25. Analysing the semantic association of audio promotes the application of folk music in occupational therapy.

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

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confidence: 99%

Retracted: Optimization of Music Teaching Management System for College Students Based on Similarity Distribution Method

Engineering

2023

Mathematical Problems in Engineering

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Optimization of Music Teaching Management System for College Students Based on Similarity Distribution Method

Cited by 4 publications

References 29 publications

Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)

Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)

Design of Semantic Matching Model of Folk Music in Occupational Therapy Based on Audio Emotion Analysis

Retracted: Optimization of Music Teaching Management System for College Students Based on Similarity Distribution Method

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