Title :
Hybrid Independent Component Analysis and Support Vector Machine Approach for Audio Classification
Author :
Zhou, Xianzhong ; He, Xin ; Shi, Yingchun
Author_Institution :
Sch. of Manage. & Eng., Nanjing Univ.
Abstract :
The recent explosive growth of multimedia information in many applications requires advanced techniques for multimedia information retrieval (MIR). Audio is one of important media in multimedia and contains abundant information. Recent development in MIR has revealed that audio information can provide effective contents to retrieve multimedia information. Consequently, it has been paid more and more attention to how to classify audio information. Many existing and new research issues can lead to improve the retrieval efficiency, effectiveness, and accessibility. This paper provides a hybrid classification method for audio indexing and retrieval. It firstly describes main audio characteristics and features and the basic theory of support vector machine (SVM) and independent component analysis (ICA). Finally, the hybrid method that integrates ICA and SVM is presented and applied into audio classification
Keywords :
independent component analysis; information retrieval; multimedia communication; support vector machines; audio indexing; audio information classification; hybrid classification method; hybrid independent component analysis; multimedia information retrieval; retrieval efficiency; support vector machine approach; Explosives; Helium; Hidden Markov models; Independent component analysis; Information retrieval; Kernel; Neural networks; Statistics; Support vector machine classification; Support vector machines;
Conference_Titel :
Networking, Sensing and Control, 2006. ICNSC '06. Proceedings of the 2006 IEEE International Conference on
Conference_Location :
Ft. Lauderdale, FL
Print_ISBN :
1-4244-0065-1
DOI :
10.1109/ICNSC.2006.1673140