Spectral density estimation

About: Spectral density estimation is a research topic. Over the lifetime, 5391 publications have been published within this topic receiving 123105 citations.

Improved low-frequency decay estimation using the multitaper spectral analysis method1

Abstract: Spectral analysis is one of the most ubiquitous signal processing tools used in exploration geophysics. Among many applications, it is used simply to look at the frequency content of seismic traces, to find notches, to estimate wavelets under the minimum-phase assumption, and to match broadband synthetic seismograms to seismic data. Seismic spectra exhibit very large dynamic ranges, particularly at low frequencies. Estimation of low-frequency decay is very important for accurate modelling. However, when using traditional spectral estimates incorporating smoothing windows, too much sidelobe energy leaks from high power into low power areas, spoiling our ability to estimate low-frequency spectral decay. The multitaper method of spectral analysis due to D. Thomson does not employ just a single window, but rather a set of orthogonal data tapers. It is possible to have much less sidelobe contamination, while maintaining a stable estimate. The trace is tapered by each of a subset of the orthogonal tapers, and a raw spectral estimate produced in each case. These are combined to produce a final spectral estimate. The technique can be made adaptive by applying different weights to the different raw spectra at different frequencies. A comparison of seismic spectral estimation using this multitaper technique with a traditional approach having the same analysis bandwidth and stability demonstrates the very different estimates of spectral decay in the areas of high dynamic range. The multitaper approach provides estimates with much reduced sidelobe leakage, and hence is a very appealing method for reflection seismology.

X-ray imaging apparatus, x-ray imaging method, and x-ray imaging program

Abstract: An X-ray imaging apparatus includes a phase grating, an absorption grating, a detector, and an arithmetic unit. The arithmetic unit executes a Fourier transform step of performing Fourier transform for an intensity distribution of a Moire acquired by the detector, and acquiring a spatial frequency spectrum. Also, the arithmetic unit executes a phase retrieval step of separating a spectrum corresponding to a carrier frequency from a spatial frequency spectrum acquired in the Fourier transform step, performing inverse Fourier transform for the separated spectrum, and acquiring a differential phase image.

Composite wavefront decomposition via multidimensional digital filtering of array data

Abstract: A solution is obtained to the problem of estimating the number, vector velocity, and waveshape of overlapping planewaves in the presence of interfering planewaves and channel noise, where previous solutions have assumed one or more of these quantities as known. A general optimum solution is not found; instead, a heuristic solution is presented along with a complete working implementation program for large scale computers. For the case where the number of waves and the vector velocities are known, the solution is optimum. The detection of waves and the estimation of their bearing, velocity, and waveshape is accomplished via digital filtering of the frequencywavenumber power spectrum, which is computed via an efficient estimator, of the array sensed data. A new approach to the multiwave estimation problem is to reduce it to a succession of single wave problems using especially developed frequency-wavenumber filters. Special attention is given throughout the study to computationally efficient approaches. The results of the paper are placed in perspective by showing how the historically important approaches to the processing of array data such as delay and sum, weighted delay and sum, array prewhitening, beam forming, inverse filtering, least mean-square estimation, and maximum likelihood estimation are related via the spatio-temporal filtering of the frequency-wavenumber spectrum. The spectral estimation, digital filtering, and the multiwave maximum likelihood estimator developments are demonstrated by the processing of a set of simulated planewaves of various bearings, velocities, and frequencies, as well as by processing electroencephalographic (brain wave) data monitored via an array of scalp electrodes.

The effects of subtractive-type speech enhancement/noise reduction algorithms on parameter estimation for improved recognition and coding in high noise environments

Abstract: The authors present the results of a study designed to investigate the effects of subtractive-type noise reduction algorithms on LPC-based spectral parameter estimation as related to the performance of speech processors operating with input SNRs of 15 dB and below. Subtractive noise preprocessing greatly improves the SNR, but system performance improvement is not commensurate. LPC spectral estimation is affected by the character of the residual noise which exhibits greater variance and spectral granularity than the original broadband noise. The study shows that removing less than the full amount of noise and whitening it improves spectral estimation and speech device performance. Techniques and performance results are presented. >