Comparison and Analysis of Acoustic Features of Western and Chinese Classical Music Emotion Recognition Based on V-A Model

Abstract

Music emotion recognition is increasingly becoming important in scientific research and practical applications. Due to the differences in musical characteristics between Western and Chinese classical music, it is necessary to investigate the distinctions in music emotional feature sets to improve the accuracy of cross-cultural emotion recognition models. Therefore, a comparative study on emotion recognition in Chinese and Western classical music was conducted. Using the V-A model as an emotional perception model, approximately 1000 pieces of Western and Chinese classical excerpts in total were selected, and approximately 20-dimension feature sets for different emotional dimensions of different datasets were finally extracted. We considered different kinds of algorithms at each step of the training process, from pre-processing to feature selection and regression model selection. The results reveal that the combination of MaxAbsScaler pre-processing and the wrapper method using the recursive feature elimination algorithm based on extremely randomized trees is the optimal algorithm. The harmonic change detection function is a culturally universal feature, whereas spectral flux is a culturally specific feature for Chinese classical music. It is also found that pitch features are more significant for Western classical music, whereas loudness and rhythm features are more significant for Chinese classical music.

Keywords

• music emotion recognition
• classical music
• V-A model
• feature selection
• extreme random tree