Research and Implementation of Emotional Classification of Traditional Folk Songs Based on Joint Time-Frequency Analysis

Abstract

Traditional folk music is built on traditional folk songs. The five types of emotions of patriotism, homesickness, affection, friendship, and love are all depicted in modern Chinese folk songs, which show the rich and colorful emotions of people of all ethnic groups in China. Modern Chinese folk songs that express these emotions have a variety of artistic features in terms of melody, rhythm, mode, and mood and provide the audience with unique aesthetic experiences. This paper focuses on JTFA- (Joint Time-Frequency Analysis-) based emotional classification of traditional folk songs, as well as PCA (principal component analysis) and KPCA (Kernel-Based Principle Component Analysis) methods for nonstationary signal feature dimension reduction. The simulation results show that the number of features in KPCA is less than in PCA for the same accuracy. When the number of features is equal to the number of principal components, the accuracy is higher than PCA, indicating that KPCA has a better effect in dimension reduction and feature extraction. Furthermore, in all categories, the double-layer classification model maintains a relatively high recall rate and accuracy rate, demonstrating the effectiveness of the double-layer multimusic emotion classification model.