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. 2022 Aug 17:2022:5764148.
doi: 10.1155/2022/5764148. eCollection 2022.

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Biyun Xue  1 , Ye Song  2
Affiliations

Research on the Filtering and Classification Method of Interactive Music Education Resources Based on Neural Network

Biyun Xue et al. Comput Intell Neurosci. .

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Abstract

This work intends to classify and integrate music genres and emotions to improve the quality of music education. This work proposes a web image education resource retrieval method based on semantic network and interactive image filtering for a music education environment. It makes a judgment on these music source data and then uses these extracted feature sequences as the emotions expressed in the model of the combination of Long Short-Term Memory (LSTM) and Attention Mechanism (AM), thus judging the emotion category of music. The emotion recognition accuracy has increased after improving LSTM-AM into the BiGR-AM model. The greater the difference between emotion genres is, the easier it is to analyze the feature sequence containing emotion features, and the higher the recognition accuracy is. The classification accuracy of the excited, relieved, relaxed, and sad emotions can reach 76.5%, 71.3%, 80.8%, and 73.4%, respectively. The proposed interactive filtering method based on a Convolutional Recurrent Neural Network can effectively classify and integrate music resources to improve the quality of music education.

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Conflict of interest statement

The authors declared that they have no conflicts of interest regarding this work.

Figures

Figure 1
Figure 1
Comparison of online and offline music education.
Figure 2
Figure 2
Proportion of piano sparring in online music sparring from 2015 to 2021.
Figure 3
Figure 3
LSTM structure.
Figure 4
Figure 4
AM structure.
Figure 5
Figure 5
Emotion-based music classification process.
Figure 6
Figure 6
Mel's music clips of songs of different genres. (a) Pop, (b) blues, (c) jazz, and (d) disco.
Figure 7
Figure 7
Comparison of music emotion discrimination results obtained from different models.
Figure 8
Figure 8
Comparison of accuracy of different pooling methods.
Figure 9
Figure 9
Comparison of accuracy of different pooling methods.
See this image and copyright information in PMC

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