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

Yuechi Jiang, H. F. Frank Leung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


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.

Original languageEnglish
Title of host publication2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538668115
Publication statusPublished - 19 Nov 2018
Event23rd IEEE International Conference on Digital Signal Processing, DSP 2018 - Shanghai, China
Duration: 19 Nov 201821 Nov 2018

Publication series

NameInternational Conference on Digital Signal Processing, DSP


Conference23rd IEEE International Conference on Digital Signal Processing, DSP 2018


  • audio signal classification
  • Fisher discriminant analysis
  • modified Fisher discriminant analysis
  • new between-class scatter matrix

ASJC Scopus subject areas

  • Signal Processing

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