Fisher Discriminant Analysis with New Between-class Scatter Matrix for Audio Signal Classification

Abstract

Fisher Discriminant Analysis (FDA) is a widely used technique for signal classification. Its application varies from face recognition to speaker recognition. FDA aims to project a given feature onto a projected space, where the features coming from the same class are moved closer, while those coming from different classes are moved farther. However, in the original formulation of FDA, the number of orthogonal projection directions is limited by the number of classes, which may hinder the effectiveness of FDA as a projection technique. In this paper, we propose to use new between-class scatter matrices to replace the original between-class scatter matrix, in order to increase the number of orthogonal projection directions. We call FDA with these new between-class scatter matrices the Modified FDA (MFDA). The effectiveness of MFDA and FDA as a projection technique is compared through doing two audio signal classification tasks. Both linear version and kernel version of MFDA and FDA are evaluated, and experimental results show that MFDA can outperform FDA in both classification tasks.