Estimation of acoustic echoes using expectation-maximization methods

Refactoring acoustic models using variational expectation-maximization

In probabilistic modeling, it is often useful to change the structure, or refactor, a model, so that it has a different number of components, different parameter sharing, or other constraints. For example, we may wish to find a Gaussian mixture model (GMM) with fewer components that best approximates a reference model. Maximizing the likelihood of the refactored model under the reference model ...

Hand Pose Estimation Using Expectation-Constrained-Maximization From Voxel Data

This paper describes voxel-based hand posture estimation using the Expectation Constrained-Maximization framework to estimate the parameters to a 16 segment 24 degreesof-freedom hand model. This method uses only voxels to estimate finger segment locations and orientations. The hand model consists of a mixture of kinematically constrained 3D gaussian distributions. Results on simulated and real ...

Convolutional Expectation Maximization for Population Estimation

There is a fundamental spatial mismatch in the data available for estimate population from satellite imagery. Spectral reflectances are available for each pixel of an image, but ground reference population data are available only for larger zones, therefore satellite imagery has bigger resolution than ground reference images. The general response has been to build models for the average populat...

Expectation Maximization

Expectation Maximization

The Expectation Maximization (EM) algorithm [1, 2] is one of the most widely used algorithms in statistics. Suppose we are given some observed data X and a model family parametrized by θ, and would like to find the θ which maximizes p(X |θ), i.e. the maximum likelihood estimator. The basic idea of EM is actually quite simple: when direct maximization of p(X |θ) is complicated we can augment the...