The generalized bayes method for high-dimensional data recognition with applications to audio signal recognition

Hsiuying Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application.

Original languageEnglish
Article number19
Pages (from-to)1-11
Number of pages11
JournalSymmetry
Volume13
Issue number1
DOIs
StatePublished - Jan 2021

Keywords

  • Gaussian mixture model
  • Generalized Bayes estimator
  • Maximum likelihood estimator
  • Recognition rate

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