DocumentCode
169746
Title
Music Emotion Classification Based on Music Highlight Detection
Author
Jun-Yong Lee ; Ji-Yeon Kim ; Hyoung-Gook Kim
Author_Institution
Dept. of Electron. Convergence Eng., Kwangwoon Univ., Seoul, South Korea
fYear
2014
fDate
6-9 May 2014
Firstpage
1
Lastpage
2
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.
Keywords
audio signal processing; discrete cosine transforms; learning (artificial intelligence); music; signal classification; AdaBoost algorithm; energy information; modified discrete cosine transform; music emotion classification; music highlight segment detection; normalized MDCT coefficients; tempo feature; timbre feature; Convergence; Educational institutions; Feature extraction; Recommender systems; Timbre; Transforms;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Applications (ICISA), 2014 International Conference on
Conference_Location
Seoul
Print_ISBN
978-1-4799-4443-9
Type
conf
DOI
10.1109/ICISA.2014.6847435
Filename
6847435
Link To Document