The Generalized Bayes Method for High-Dimensional Data Recognition with Applications to Audio Signal Recognition

• Main Author: Hsiuying Wang
• Format: Article
• Language: English
• Published: MDPI AG 2021-12-01
• Series: Symmetry
• Subjects: Gaussian mixture model; maximum likelihood estimator; generalized Bayes estimator; recognition rate
• Online Access: https://www.mdpi.com/2073-8994/13/1/19

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.