DocumentCode
418177
Title
Boosting as a dimensionality reduction tool for audio classification
Author
Ravindran, Sourabh ; Anderson, David V.
Author_Institution
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Volume
3
fYear
2004
fDate
23-26 May 2004
Abstract
In this paper we present a modified AdaBoost algorithm that can be used for dimensionality reduction in audio classification problems. The algorithm is modified to work as a feature selector for a four way classification problem. It is compared with principal component analysis (PCA), which is a popular tool for reducing the dimensions of high-dimensional data without losing significant scatter information. Both algorithms are applied to a four way audio classification problem and the results are presented.
Keywords
audio signal processing; principal component analysis; signal classification; AdaBoost algorithm; dimensionality reduction tool; feature selector; four way audio classification; high-dimensional data; principal component analysis; scatter information; Boosting; Clustering algorithms; Covariance matrix; Matrix decomposition; Pattern classification; Principal component analysis; Scattering; Singular value decomposition;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on
Print_ISBN
0-7803-8251-X
Type
conf
DOI
10.1109/ISCAS.2004.1328784
Filename
1328784
Link To Document