Music Style Classification with Compared Methods in XGB and BPNN
This is an incremental improvement for music classification tasks.
The paper tackled music style classification by comparing XGB and BPNN classifiers, finding that XGB performs better on small datasets.
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