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

Xi Wang

doi: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 2 6 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.

Highlights

As an art form, music can express people's thoughts, feelings, and life style and has a role in promoting people’s emotion and spirit
The results showed that support vector machine (SVM) had significant advantages in the classification and recognition of folk music
To obtain the optimal parameters of SVM, this study analyzed the influence of different values on the results by the cross-check method, and the obtained optimal parameters were used for the step of the experiment

Summary

Introduction

As an art form, music can express people's thoughts, feelings, and life style and has a role in promoting people’s emotion and spirit. Iloga et al [4] studied the genre classification of music, designed a sequential pattern mining method, and carried out experiments on GTZAN. They found that the accuracy of the method was 91.6%, which was more than 7% higher than the existing classifiers. This study took folk music as the research subject, carried out feature extraction in aspects of time domain and frequency domain, established a feature database, and identified and classified folk music with a support vector machine (SVM), and verified the reliability of the method through experiments. The present study contributes to the realization of the automatic classification of folk music and the improvement of retrieval efficiency

Results

Discussion

Conclusion