Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 10, Issue 1


Published
on

March 21, 2025


Pages


DOI

Article

Research on Pattern Recognition Methods of Traditional Music Style Characteristics in Big Data Environment

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Authors

Qinfei Han Affiliation:
Conservatory of Music, Youngnam University, Gyeongsan, Daegu, 38541, Korea.
and Ning Wang Affiliation:
School of Music, Linyi University, Linyi, Shandong, 276000, China.


Abstract

Traditional music is a cultural treasure emerging from the long history of mankind, and the study of traditional music has important artistic and humanistic values. In this paper, the SVM algorithm under incremental learning is used to construct a traditional music style pattern recognition model using the extracted traditional music style feature parameters. The database is constructed by using traditional music compositions containing five music styles, and the data are pre-emphasized and pre-processed by adding windows and frames. After extracting the time-domain feature parameters and MFCC feature parameters of the database songs, the recognition model constructed in this paper is used for traditional music style pattern recognition. In traditional music style recognition, the accuracy of this paper’s model for five traditional music styles is around 90%, and the accuracy of traditional music recognition for opera style is as high as 95.11%. Overall, the model constructed in this paper is able to effectively recognize the styles of traditional music through the extracted traditional music style feature parameters.


Keywords

Incremental learning, SVM algorithm, MFCC, Windowed frames, Traditional music style, Pattern recognition, 68T45


Citation

Han, Q. & Wang, N. (2025). Research on pattern recognition methods of traditional music style characteristics in big data environment. Applied Mathematics and Nonlinear Sciences, 10(1). https://doi.org/10.2478/amns-2025-0658

Published by: Engineering Journals

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