GMM basedbayesian approach to speech enhancement in signal/transform domain

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator.The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

A Multi-layer Perceptron based Non-linear Mixture Model to estimate class abundance from mixed pixels

Sub-pixel classification is essential for the successful description of many land cover (LC) features with spatial resolution less than the size of the image pixels. A commonly used approach for sub-pixel classification is linear mixture models (LMM). Even though, LMM have shown acceptable results, pragmatically, linear mixtures do not exist. A non-linear mixture model, therefore, may better describe the resultant mixture spectra for endmember (pure pixel) distribution. In this paper, we propose a new methodology for inferring LC fractions by a process called automatic linear-nonlinear mixture model (AL-NLMM). AL-NLMM is a three step process where the endmembers are first derived from an automated algorithm. These endmembers are used by the LMM in the second step that provides abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual proportions are fed to multi-layer perceptron (MLP) architecture as input to train the neurons which further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. AL-NLMM is validated on computer simulated hyperspectral data of 200 bands. Validation of the output showed overall RMSE of 0.0089±0.0022 with LMM and 0.0030±0.0001 with the MLP based AL-NLMM, when compared to actual class proportions indicating that individual class abundances obtained from AL-NLMM are very close to the real observations.

Speech segmentation using extrema based signal track length measure

We introduce a novel temporal feature of a signal, namely extrema-based signal track length (ESTL) for the problem of speech segmentation. We show that ESTL measure is sensitive to both amplitude and frequency of the signal. The short-time ESTL (ST_ESTL) shows a promising way to capture the significant segments of speech signal, where the segments correspond to acoustic units of speech having distinct temporal waveforms. We compare ESTL based segmentation with ML and STM methods and find that it is as good as spectral feature based segmentation, but with lesser computational complexity.

Electrochemical performance of mixed crystallographic phase nanotubes and nanosheets of titania and titania-carbon/silver composites for lithium-ion batteries

The role of homogeneity in ex situ grown conductive coatings and dimensionality in the lithium storage properties of TiO(2) is discussed here. TiO(2) nanotube and nanosheet comprising of mixed crystallographic phases of anatase and TiO(2) (B) have been synthesized by an optimized hydrothermal method. Surface modifications of TiO(2) nanotube are realized via coating the nanotube with Ag nanoparticles and amorphous carbon. The first discharge cycle capacity (at current rate = 10 mA g(-1)) for TiO(2) nanotube and nanosheet were 355 mAh g(-1) and 225 mAhg(-1), respectively. The conductive surface coating stabilized the titania crystallographic structure during lithium insertion-deinsertion processes via reduction in the accessibility of lithium ions to the trapping sites. The irreversible capacity is beneficially minimized from 110 mAh g(-1) for TiO(2) nanotubes to 96 mAh g(-1) and 57 mAhg(-1) respectively for Ag and carbon modified TiO(2) nanotubes. The homogeneously coated amorphous carbon over TiO(2) renders better lithium battery performance than randomly distributed Ag nanoparticles coated TiO(2) due to efficient hopping of electrons. (C) 2011 Elsevier B.V. All rights reserved.

Functionalized and Postfunctionalizable Porous Polymeric Films through Evaporation-Induced Phase Separation Using Mixed Solvents

Condensation of water droplets during rapid evaporation of a polymer solution, under humid conditions, has been known to generate uniformly porous polymer films. Similar porous films are also formed when a solution of the polymer in THF containing small amounts of water, is allowed to evaporate rapidly under air flow; this suggests that water droplets may be formed during the final stages of film formation. In the presence of added surfactants, the interface of water droplets could become lined with the surfactants and consequently the internal walls of the pores generated, upon removal of the water, could become decorated with the hydrophilic head groups of the surfactant molecules. In a series of carefully designed experiments, we have examined the effect of added surfactants, both anionic and cationic, on the formation of porous PMMA films; the films were prepared by evaporating a solution of the polymer in THF containing controlled amounts of aqueous surfactant solutions. We observed that the average size of the pores decreases with increasing surfactant concentration, while it increases with increasing amounts of added water. The size of the pores and their distribution were examined using AFM and IR imaging methods. Although IR imaging possessed inadequate resolution to confirm the presence of surfactants at the pore surface, exchange of the inorganic counterion, such as the sodium-ion of SDS, with suitable ionic organic dyes permitted the unequivocal demonstration of the presence of the surfactants at the interface by the use of confocal fluorescence microscopy.

Mixed ionic and electronic conduction in zirconia and its applications in metallurgy

Mixed ionic and electronic conduction in Zr02-based solid electrolytes was studied.The effect of impurities and second-phase particles on the mixed conduction parameter, P,, was measured for different types of ZrOZ electrolytes. The performance of solid-state sensors incorporating ZrOZ electrolytes is sometimes limited by electronic conduction in ZrOZ, especially at temperatures >I800 K. Methods for eliminating or minimizing errors in measured emf due to electronically driven transport of oxygen anions are discussed. Examples include probes for monitoring oxygen content in liquid steel as well as the newly developed sulfur sensor based on a ZrOz(Ca0) + CaS electrolyte. The use of mixed conducting ZrOZ as a semipermeable membrane or chemically selective sieve for oxygen at high temperatures is discussed. Oxygen transport from liquid iron to CO + C& gas mixtures through a ZrOZ membrane driven by a chemical potential gradient, in the absence of electrical leads or imposed potentials, was experimentally observed.

Physical chemistry of mixed conducting zirconia ceramics

Suivant la pression partielle d'oxygène, la zircone peut être conducteur ionique ou électronique. Mise au point de méthodes de mesures de f.é.m. permettant de s'affranchir des sources d'erreur introduites par ces propriétés.

Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals

In this paper, we develop a low-complexity message passing algorithm for joint support and signal recovery of approximately sparse signals. The problem of recovery of strictly sparse signals from noisy measurements can be viewed as a problem of recovery of approximately sparse signals from noiseless measurements, making the approach applicable to strictly sparse signal recovery from noisy measurements. The support recovery embedded in the approach makes it suitable for recovery of signals with same sparsity profiles, as in the problem of multiple measurement vectors (MMV). Simulation results show that the proposed algorithm, termed as JSSR-MP (joint support and signal recovery via message passing) algorithm, achieves performance comparable to that of sparse Bayesian learning (M-SBL) algorithm in the literature, at one order less complexity compared to the M-SBL algorithm.

On a programmable signal processor for VLSI

This paper presents a method of designing a programmable signal processor based on a bit parallel matrix vector matrix multiplier (linear transformer). The salient feature of this design is that the efficiency of the direct vector matrix multiplier is improved and VLSI design is made much simpler by trading off the more expensive arithematic operation (multiplication) for 'cheaper' manipulation (addition/subtraction) of the data.