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

• Published in: Journal of advanced computational intelligence and intelligent informatics Vol. 29; no. 1; pp. 33 - 40
• Main Authors: Liu, Lin, Liang, Hao
• Format: Journal Article
• Language: English
• Published: Tokyo Fuji Technology Press Co. Ltd 20.01.2025
• Subjects: Algorithms; Classification; Folk music; Frequency domain analysis; Regional analysis; Support vector machines; Time-frequency analysis
• ISSN: 1343-0130; 1883-8014
• DOI: 10.20965/jaciii.2025.p0033

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.