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JACIII Vol.29 No.1 pp. 33-40
doi: 10.20965/jaciii.2025.p0033
(2025)

Research Paper:

Analysis of the Style Characteristics of Regional Folk Songs and Music Classification Algorithms

Lin Liu and Hao Liang

Conservatory of Music, Beihua University
3999 Binjiang East Road, Jilin 132013, China

Corresponding author

Received:
July 1, 2024
Accepted:
September 25, 2024
Published:
January 20, 2025
Keywords:
folk song, region, style characteristic, music classification, neural network
Abstract

Regional folk songs have a rich history and are filled with cultural values. In this paper, first, the style characteristics of regional folk songs are briefly introduced. Using four regional folk songs from the northwest, northeast, southwest, and Hakka as examples, time domain, frequency domain, and mel-frequency cepstral coefficient (MFCC) features were extracted. Finally, the bidirectional long short-term memory (BiLSTM)-based music classification algorithm is used to realize the classification of folk songs from different regions. It was found that using the time-frequency domain + MFCC as features produced better results in music classification than using only the time-frequency domain or only MFCC features. The BiLSTM algorithm achieved an accuracy of 0.8339 and an F1 value of 0.8201 for the 10 s fragment set, both of which were better than those of the K-nearest neighbor, support vector machine, and other classification algorithms. The results show that the approach used in this study to categorize regional folk songs is reliable and that it can be applied to real folk songs.

Cite this article as:
L. Liu and H. Liang, “Analysis of the Style Characteristics of Regional Folk Songs and Music Classification Algorithms,” J. Adv. Comput. Intell. Intell. Inform., Vol.29 No.1, pp. 33-40, 2025.
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Last updated on Feb. 07, 2025