Title: Classification method of popular music score style based on SVM

Authors: Qiang Tuo; Xiaoming Zhao

Addresses: Music Department of Art College, Hebei Agricultural University, Baoding 071000, Hebei Province, China ' Music Department of Art College, Hebei Agricultural University, Baoding 071000, Hebei Province, China

Abstract: In order to improve the classification accuracy of popular music score style, this paper proposes a new classification method of popular music score style based on SVM. Firstly, the fractional spectral subtraction algorithm is used to enhance the popular music score signal. Secondly, according to the unique characteristics of the striking component and the harmony part in the pop music spectrum, the striking part and the harmony part in the pop music spectrum are separated by means of energy peak removal and peak frequency filtering. Finally, taking the striking part and harmony part of the separated pop music spectrum as input, the SVM algorithm is used to realise the style classification of pop music. The experimental results show that this method can enhance the popular music score signal, and can effectively classify different styles of popular music score, with a classification accuracy of 99.4%.

Keywords: support vector machine; pop music; music score style; classification method; kernel function; classification function.

DOI: 10.1504/IJRIS.2023.136365

International Journal of Reasoning-based Intelligent Systems, 2023 Vol.15 No.3/4, pp.304 - 312

Received: 29 Aug 2022
Accepted: 27 Oct 2022
Published online: 31 Jan 2024 *

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