Music Style Classification with Compared Methods in XGB and BPNN

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