An evaluation of objective measures for intelligibility prediction of time-frequency weighted noisy speech

Existing objective speech-intelligibility measures are suitable for several types of degradation, however, it turns out that they are less appropriate in cases where noisy speech is processed by a time-frequency weighting. To this end, an extensive evaluation is presented of objective measure for intelligibility prediction of noisy speech...

Action unit classification using active appearance models and conditional random fields

In this paper, we investigate to what extent modern computer vision and machine learning techniques can assist social psychology research by automatically recognizing facial expressions. To this end, we develop a system that automatically recognizes the action units defined in the facial action coding system (FACS). The system uses a...

Camera Network Coverage Improving by Particle Swarm Optimization

This paper studies how to improve the field of view (FOV) coverage of a camera network. We focus on a special but practical scenario where the cameras are randomly scattered in a wide area and each camera may adjust its orientation but cannot move in any direction. We propose a particle swarm optimization (PSO) algorithm which can efficiently...

Articulated Planar Reformation for Change Visualization in Small Animal Imaging

The analysis of multi-timepoint whole-body small animal CT data is greatly complicated by the varying posture of the subject at different timepoints. Due to these variations, correctly relating and comparing corresponding regions of interest is challenging. In addition, occlusion may prevent effective visualization of these regions of interest....

Capturing Appearance Variation in Active Appearance Models

The paper presents an extension of active appearance models (AAMs) that is better capable of dealing with the large variation in face appearance that is encountered in large multi-person face data sets. Instead of the traditional PCA-based texture model, our extended AAM employs a mixture of probabilistic PCA to describe texture variation,...

Low Complexity DFT-Domain Noise PSD Tracking Using High-Resolution Periodograms

Although most noise reduction algorithms are critically dependent on the noise power spectral density (PSD), most procedures for noise PSD estimation fail to obtain good estimates in nonstationary noise conditions. Recently, a DFT-subspace-based method was proposed which improves noise PSD estimation under these conditions. However, this...

On Optimal Multichannel Mean-Squared Error Estimators for Speech Enhancement

In this letter we present discrete Fourier transform (DFT) domain minimum mean-squared error (MMSE) estimators for multichannel noise reduction. The estimators are derived assuming that the clean speech magnitude DFT coefficients are generalized-Gamma distributed. We show that for Gaussian distributed noise DFT coefficients, the optimal...

Sign Language Recognition by Combining Statistical DTW and Independent Classification

To recognize speech, handwriting, or sign language, many hybrid approaches have been proposed that combine Dynamic Time Warping (DTW) or Hidden Markov Models (HMMs) with discriminative classifiers. However, all methods rely directly on the likelihood models of DTW/HMM. We hypothesize that time warping and classification should be separated...

Segmentation of myocardial perfusion MR sequences with multi-band Active Appearance Models driven by spatial and temporal features

This work investigates knowledge driven segmentation of cardiac MR perfusion sequences. We build upon previous work on multi-band AAMs to integrate into the segmentation both spatial priors about myocardial shape as well as temporal priors about characteristic perfusion patterns. Different temporal and spatial features are developed without a...

Noise Tracking Using DFT Domain Subspace Decompositions

All discrete Fourier transform (DFT) domain-based speech enhancement gain functions rely on knowledge of the noise power spectral density (PSD). Since the noise PSD is unknown in advance, estimation from the noisy speech signal is necessary. An overestimation of the noise PSD will lead to a loss in speech quality, while an underestimation will...

On the Estimation of Complex Speech DFT Coefficients Without Assuming Independent Real and Imaginary Parts

This letter considers the estimation of speech signals contaminated by additive noise in the discrete Fourier transform (DFT) domain. Existing complex-DFT estimators assume independency of the real and imaginary parts of the speech DFT coefficients, although this is not in line with measurements. In this letter, we derive some general results on...