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<p>In the age of information technology, the communication and collision mode between folk music cultures is no longer single, and the data of information also makes the dissemination of folk music culture develop towards diversification. This paper first builds a folk music appreciation platform based on B/S model system architecture. Apriori mining algorithm is added to improve the fully automatic algorithm. After constructing the database of aesthetic elements, we select and pre-process the audio data to be mined for folk music appreciation. Convert and mine the processed aesthetic elements. Finally, the mined aesthetic elements in folk music appreciation are analyzed. The test results of the improved Apriori data mining algorithm are generally high, as verified by experiments. The folk music with the highest number of clicks was “Five Brothers Herding Sheep” with 810 clicks. The ethnic music with the least number of clicks is “Yellow River”, with 189 clicks. The ethnic music with the highest number of clicks was “Five Brothers Herding Sheep”, with 290 clicks. The folk music with the least number of clicks is “Dongfang Hong”, with 76 clicks. This confirms that the Apriori mining algorithm, after improvement, has high accuracy and outstanding advantages and can be used as the main means of mining aesthetic elements in folk music appreciation. Thus, the reliability of folk music appreciation can be further improved.</p>
</abstract>

In the age of information technology, the communication and collision mode between folk music cultures is no longer single, and the data of information also makes the dissemination of folk music culture develop towards diversification. This paper first builds a folk music appreciation platform based on B/S model system architecture. Apriori mining algorithm is added to improve the fully automatic algorithm. After constructing the database of aesthetic elements, we select and pre-process the audio data to be mined for folk music appreciation. Convert and mine the processed aesthetic elements. Finally, the mined aesthetic elements in folk music appreciation are analyzed. The test results of the improved Apriori data mining algorithm are generally high, as verified by experiments. The folk music with the highest number of clicks was “Five Brothers Herding Sheep” with 810 clicks. The ethnic music with the least number of clicks is “Yellow River”, with 189 clicks. The ethnic music with the highest number of clicks was “Five Brothers Herding Sheep”, with 290 clicks. The folk music with the least number of clicks is “Dongfang Hong”, with 76 clicks. This confirms that the Apriori mining algorithm, after improvement, has high accuracy and outstanding advantages and can be used as the main means of mining aesthetic elements in folk music appreciation. Thus, the reliability of folk music appreciation can be further improved.

A Study on Aesthetic Elements in Ethnic Music Appreciation in the Age of Information Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the age of information technology, the communication and collision mode between folk music cultures is no longer single, and the data of information also...

A Study on Aesthetic Elements in Ethnic Music Appreciation in the Age of Information Technology

Publié en ligne: 30 juin 2023

Pages: -

Reçu: 03 sept. 2022

Accepté: 09 janv. 2023

DOI: https://doi.org/10.2478/amns.2023.1.00485

Mots clés
Information age, Folk music appreciation, Aesthetic elements, Data mining, Apriori mining algorithm

© 2023 Jiangli Jia et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Study on Aesthetic Elements in Ethnic Music Appreciation in the Age of Information Technology

Publié en ligne: 30 juin 2023

Pages: -

Reçu: 03 sept. 2022

Accepté: 09 janv. 2023

DOI: https://doi.org/10.2478/amns.2023.1.00485

Mots clésInformation age, Folk music appreciation, Aesthetic elements, Data mining, Apriori mining algorithm

© 2023 Jiangli Jia et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Information age, Folk music appreciation, Aesthetic elements, Data mining, Apriori mining algorithm