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人工智能背景下无线网络“学堂乐歌”智慧课堂在线教学模式研究。

Research on Classroom Online Teaching Model of "Learning" Wisdom Music on Wireless Network under the Background of Artificial Intelligence.

机构信息

Music Department of Tangshan Normal University, Tang Shan, He Bei 063000, China.

Department of Computer Science, Superior University Lahore, Pakistan.

出版信息

Comput Math Methods Med. 2021 Nov 27;2021:3141661. doi: 10.1155/2021/3141661. eCollection 2021.

Abstract

This article uses a multimodal smart music online teaching method combined with artificial intelligence to address the problem of smart music online teaching and to compensate for the shortcomings of the single modal classification method that only uses audio features for smart music online teaching. The selection of music intelligence models and classification models, as well as the analysis and processing of music characteristics, is the subjects of this article. It mainly studies how to use lyrics and how to combine audio and lyrics to intelligently classify music and teach multimodal and monomodal smart music online. In the online teaching of smart music based on lyrics, on the basis of the traditional wireless network node feature selection method, three parameters of frequency, concentration, and dispersion are introduced to adjust the statistical value of wireless network nodes, and an improved wireless network is proposed. After feature selection, the TFIDF method is used to calculate the weights, and then artificial intelligence is used to perform secondary dimensionality reduction on the lyrics. Experimental data shows that in the process of intelligently classifying lyrics, the accuracy of the traditional wireless network node feature selection method is 58.20%, and the accuracy of the improved wireless network node feature selection method is 67.21%, combined with artificial intelligence and improved wireless, the accuracy of the network node feature selection method is 69.68%. It can be seen that the third method has higher accuracy and lower dimensionality. In the online teaching of multimodal smart music based on audio and lyrics, this article improves the traditional fusion method for the problem of multimodal fusion and compares various fusion methods through experiments. The experimental results show that the improved classification effect of the fusion method is the best, reaching 84.43%, which verifies the feasibility and effectiveness of the method.

摘要

本文采用多模态智能音乐在线教学方法与人工智能结合,解决智能音乐在线教学问题,弥补了仅使用音频特征进行智能音乐在线教学的单一模态分类方法的不足。音乐智能模型和分类模型的选择,以及音乐特征的分析和处理,是本文的研究内容。主要研究如何使用歌词以及如何将音频和歌词结合起来,对音乐进行智能分类,并对多模态和单模态智能音乐进行在线教学。在基于歌词的智能音乐在线教学中,在传统无线网络节点特征选择方法的基础上,引入频率、集中和分散三个参数来调整无线网络节点的统计值,提出了一种改进的无线网络。在特征选择之后,使用 TF-IDF 方法计算权重,然后使用人工智能对歌词进行二次降维。实验数据表明,在智能歌词分类过程中,传统无线网络节点特征选择方法的准确率为 58.20%,改进的无线网络节点特征选择方法的准确率为 67.21%,结合人工智能和改进的无线网络,网络节点特征选择方法的准确率为 69.68%。可以看出,第三种方法具有更高的准确率和更低的维度。在基于音频和歌词的多模态智能音乐在线教学中,本文针对多模态融合的问题改进了传统的融合方法,并通过实验比较了各种融合方法。实验结果表明,改进的融合方法的分类效果最好,达到 84.43%,验证了该方法的可行性和有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c33c/8643225/7b5424ede43d/CMMM2021-3141661.001.jpg

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