Music Style Classification with Compared Methods in XGB and BPNN
Lifeng Tan, Cong Jin, Zhiyuan Cheng, Xin Lv, Leiyu Song

TL;DR
This paper compares the effectiveness of XGB and BPNN classifiers in music style classification, highlighting feature extraction techniques and demonstrating XGB's superior performance on small datasets.
Contribution
The study introduces a comparative analysis of XGB and BPNN for music style classification, emphasizing feature extraction methods for timbral, rhythmic, and pitch content.
Findings
XGB outperforms BPNN on small datasets
Feature extraction improves classification accuracy
Comparative evaluation validates the effectiveness of proposed methods
Abstract
Scientists have used many different classification methods to solve the problem of music classification. But the efficiency of each classification is different. In this paper, we propose two compared methods on the task of music style classification. More specifically, feature extraction for representing timbral texture, rhythmic content and pitch content are proposed. Comparative evaluations on performances of two classifiers were conducted for music classification with different styles. The result shows that XGB is better suited for small datasets than BPNN
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Taxonomy
TopicsMusic and Audio Processing · Music Technology and Sound Studies · Speech and Audio Processing
