A sawtooth waveform inspired pitch estimator (SWIPE) has been developed for speech and music. SWIPE estimates the pitch as the fundamental frequency of the sawtooth waveform whose spectrum best matches the spectrum of the input signal. The comparison of the spectra is done by computing a normalized inner product between the spectrum of the signal and a modified cosine. The size of the analysis window is chosen appropriately to make the width of the main lobes of the spectrum match the width of the positive lobes of the cosine. SWIPE('), a variation of SWIPE, utilizes only the first and prime harmonics of the signal, which significantly reduces subharmonic errors commonly found in other pitch estimation algorithms. The authors' tests indicate that SWIPE and SWIPE(') performed better on two spoken speech and one disordered voice database and one musical instrument database consisting of single notes performed at a variety of pitches.
Using clues from neurobiological adaptation, we have developed the constant-statistics (CS) algorithm for nonuniformity correction of infrared focal point arrays (IRFPAs) and other imaging arrays. The CS model provides an efficient implementation that can also eliminate much of the ghosting artifact that plagues all scene-based nonuniformity correction (NUC) algorithms. The CS algorithm with deghosting is demonstrated on synthetic and real infrared (IR) sequences and shown to improve the overall accuracy of the correction procedure.
In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.
[1] Seismoelectric phenomena in sediments arise from acoustic wave-induced fluid motion in the pore space, which perturbs the electrostatic equilibrium of the electric double layer on the grain surfaces. Experimental techniques and the apparatus built to study the conductivity dependence of the electrokinetic (EK) effect are described, and outcomes for studies in loose glass microspheres and medium-grain sand are presented. By varying the NaCl concentration in the pore fluid, we measured the conductivity dependence of two kinds of EK behavior: (1) the electric fields generated within the samples by the passage of transmitted acoustic waves and (2) the electromagnetic waves produced at the fluid-sediment interface by the incident acoustic wave. Both phenomena are caused by relative fluid motion in the sediment pores; this feature is characteristic of poroelastic (Biot) media but is not predicted by either viscoelastic fluid or solid models. A model of plane wave reflection from a fluid-sediment interface using EK-Biot theory leads to theoretical predictions that compare well to the experimental data for both loose glass microspheres and medium-grain sand.Citation: Block, G. I., and J. G. Harris (2006), Conductivity dependence of seismoelectric wave phenomena in fluid-saturated sediments,
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