Music Style Classification with Compared Methods in XGB and BPNN

2019/12/03 by Lifeng Tan, Tan, Lifeng, Cong Jin +7 · 1 citation
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music Technology and Sound Studies #Music and Audio Processing #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1912.01203

openalex publication_date 2019/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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

Citations

• Music Genre Classification using Machine Learning Techniques

Cited by

• Style-based Composer Identification and Attribution of Symbolic Music Scores: a Systematic Survey

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