TITLE:
Splitting of Gaussian Models via Adapted BML Method Pertaining to Cry-Based Diagnostic System
AUTHORS:
Hesam Farsaie Alaie, Chakib Tadj
KEYWORDS:
Adapted Boosted Mixture Learning; Gaussian Mixture Model; Splitting of Gaussians; Expected-Maximization Algorithm; Cry Signals
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
October
31,
2013
ABSTRACT: In this paper,we make use of the boosting method to
introduce a new learning algorithm for Gaussian Mixture Models (GMMs) called
adapted Boosted Mixture Learning (BML). The method possesses the ability to
rectify the existing problems in other conventional techniques for estimating
the GMM parameters, due in part to a new mixing-up strategy to increase the
number of Gaussian components. The discriminative splitting idea is employed
for Gaussian mixture densities followed by learning via the introduced method.
Then, the GMM classifier was applied to distinguish between healthy infants and
those that present a selected set of medical conditions. Each group includes
both full-term and premature infants. Cry-pattern for each pathological
condition is created by using the adapted BML method and 13-dimensional
Mel-Frequency Cepstral Coefficients (MFCCs) feature vector. The test results
demonstrate that the introduced method for training GMMs has a better
performance than the traditional method based upon random splitting and EM-based
re-estimation as a reference system in multi-pathological classification task.