Robust estimation of a global Gaussian mixture by decentralized aggregations of local models

2013 ◽  
Vol 11 (3) ◽  
pp. 245-262
Author(s):  
Ali El-Attar ◽  
Antoine Pigeau ◽  
Marc Gelgon
Keyword(s):  
Robust Estimation ◽  
Gaussian Mixture ◽  
Local Models

scholarly journals Estimation and computations for Gaussian mixtures with uniform noise under separation constraints

2021 ◽  
Author(s):  
Pietro Coretto
Keyword(s):  
Data Analysis ◽  
Density Estimation ◽  
Robust Estimation ◽  
Data Distribution ◽  
Gaussian Mixture ◽  
Gaussian Mixtures ◽  
Outlier Identification ◽  
Uniform Noise ◽  
Type Algorithm ◽  
Ml Estimator

AbstractIn this paper we study a finite Gaussian mixture model with an additional uniform component that has the role to catch points in the tails of the data distribution. An adaptive constraint enforces a certain level of separation between the Gaussian mixture components and the uniform component representing noise and outliers in the tail of the distribution. The latter makes the proposed tool particularly useful for robust estimation and outlier identification. A constrained ML estimator is introduced for which existence and consistency is shown. One of the attractive features of the methodology is that the noise level is estimated from data. We also develop an EM-type algorithm with proven convergence. Based on numerical evidence we show how the methods developed in this paper are useful for several fundamental data analysis tasks: outlier identification, robust location-scale estimation, clustering, and density estimation.

Robust Estimation of Integrated Hydraulics and Parameters in Water Distribution Systems

2001 ◽  
Author(s):  
Mietek A. Brdys ◽  
Kazimierz Duzinkiewicz ◽  
Michal Grochowski ◽  
Tomasz Rutkowski
Keyword(s):  
Robust Estimation ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems

Speaker Identity Recognition by Acoustic and Visual Data Fusion through Personal Privacy for Smart Care and Service Applications

2020 ◽  
Vol 64 (4) ◽  
pp. 40404-1-40404-16
Author(s):  
I.-J. Ding ◽  
C.-M. Ruan
Keyword(s):  
Face Detection ◽  
Speaker Recognition ◽  
Visual Information ◽  
Classification Tree ◽  
Gaussian Mixture ◽  
Recognition Method ◽  
Indoor Space ◽  
Identity Recognition ◽  
Visual Identity ◽  
Speaker Classification

Abstract With rapid developments in techniques related to the internet of things, smart service applications such as voice-command-based speech recognition and smart care applications such as context-aware-based emotion recognition will gain much attention and potentially be a requirement in smart home or office environments. In such intelligence applications, identity recognition of the specific member in indoor spaces will be a crucial issue. In this study, a combined audio-visual identity recognition approach was developed. In this approach, visual information obtained from face detection was incorporated into acoustic Gaussian likelihood calculations for constructing speaker classification trees to significantly enhance the Gaussian mixture model (GMM)-based speaker recognition method. This study considered the privacy of the monitored person and reduced the degree of surveillance. Moreover, the popular Kinect sensor device containing a microphone array was adopted to obtain acoustic voice data from the person. The proposed audio-visual identity recognition approach deploys only two cameras in a specific indoor space for conveniently performing face detection and quickly determining the total number of people in the specific space. Such information pertaining to the number of people in the indoor space obtained using face detection was utilized to effectively regulate the accurate GMM speaker classification tree design. Two face-detection-regulated speaker classification tree schemes are presented for the GMM speaker recognition method in this study—the binary speaker classification tree (GMM-BT) and the non-binary speaker classification tree (GMM-NBT). The proposed GMM-BT and GMM-NBT methods achieve excellent identity recognition rates of 84.28% and 83%, respectively; both values are higher than the rate of the conventional GMM approach (80.5%). Moreover, as the extremely complex calculations of face recognition in general audio-visual speaker recognition tasks are not required, the proposed approach is rapid and efficient with only a slight increment of 0.051 s in the average recognition time.

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