Title :
Research on fast music classification based on SVM in compressed domain
Author :
Chang, Liaoyu ; Yu, Xiaoqing ; Wan, Wanggen ; Yao, Jincao
Author_Institution :
Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai
Abstract :
In this paper we make a research on svm algorithm used for music genre classification in mp3 compressed domain and analyze parameters combinationpsilas impact on music classification accuracy, number of support vectors and classification time. Based on the analysis, we propose a new method how to realize fast and effective music classification by svm in compressed domain. This method greatly improves the speed while the classification accuracy is guaranteed.
Keywords :
audio coding; multimedia computing; music; pattern classification; support vector machines; MP3 compressed domain; SVM algorithm; fast music classification; music classification accuracy; music genre classification; Cepstral analysis; Data mining; Electronic mail; Electronics industry; Feature extraction; Indexing; Mel frequency cepstral coefficient; Multiple signal classification; Support vector machine classification; Support vector machines;
Conference_Titel :
Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-1723-0
Electronic_ISBN :
978-1-4244-1724-7
DOI :
10.1109/ICALIP.2008.4590230
