Research on Recognition and Classification of Folk Music Based on Feature Extraction Algorithm

Authors

  • Xi Wang

DOI:

https://doi.org/10.31449/inf.v44i4.3388

Abstract

In this study, the feature extraction algorithm for folk music was analyzed. The features of folk music were extracted in aspects of time domain and frequency domain. Then, a support vector machine (SVM) was selected to identify and classify folk music. It was found that the performance of SVM was the best when  was 26 and was 4; the recognition rate of using only one feature was inferior to that of using all features; the highest recognition rate of SVM was 92.76%; compared with back propagation neural network (BPNN) and decision tree classification method, SVM had a higher recognition rate. The experimental results show the effectiveness of SVM, which can be applied in practice.

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Published

2020-12-15

How to Cite

Wang, X. (2020). Research on Recognition and Classification of Folk Music Based on Feature Extraction Algorithm. Informatica, 44(4). https://doi.org/10.31449/inf.v44i4.3388

Issue

Section

Student papers