Multi-level audio classification architecture

Abstract

A multi-level classification architecture for solving binary discrimination problem is proposed in this paper. The main idea of proposed solution is derived from the fact that solving one binary discrimination problem multiple times can reduce the overall miss-classification error. We aimed our effort towards building the classification architecture employing the combination of multiple binary SVM (Support Vector Machine) classifiers for solving two-class discrimination problem. Therefore, we developed a binary discrimination architecture employing the SVM classifier (BDASVM) with intention to use it for classification of broadcast news (BN) audio data. The fundamental element of BDASVM is the binary decision (BD) algorithm that performs discrimination between each pair of acoustic classes utilizing decision function modeled by separating hyperplane. The overall classification accuracy is conditioned by finding the optimal parameters for discrimination function resulting in higher computational complexity. The final form of proposed BDASVM is created by combining four BDSVM discriminators supplemented by decision table. Experimental results show that the proposed classification architecture can decrease the overall classification error in comparison with binary decision trees SVM (BDTSVM) architecture.