Music Emotion Classification Based on Music Highlight Detection

Abstract

This paper presents a music emotion classification based on music highlight detection. To find a highlight segment of songs, we use only energy information based on normalized MDCT coefficients of audio streams. With AdaBoost algorithm, the proposed tempo feature is combined with timbre features and improves the performance of music emotion classification based on the detected music highlight segment. Experimental results confirm that the proposed method achieves preliminary promising results in terms of accuracy.