Topic
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.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, it is shown that the maximum-likelihood estimation or robust estimation of the Fourier coefficients may be preferable to Fourier transformation if the noise contains outliers or is otherwise not normally distributed.
Abstract: It is shown that the maximum-likelihood estimation or robust estimation of the Fourier coefficients may be preferable to Fourier transformation if the noise contains outliers or is otherwise not normally distributed. The reason is that, in that case, these estimators produce Fourier coefficient estimates and, therefore, system parameter estimates having a smaller variance. >
33 citations
TL;DR: Frequency, amplitudes, phases, and damping factors are estimated by applying a generalization of the monodimensional Prony's method to spatial data by finding the desired quantities after an autoregressive model fitting to the data, a polynomial rooting, and a least-squares problem solution.
Abstract: The problem of estimating the parameters of a model for bidimensional data made up by a linear combination of damped two-dimensional sinusoids is considered. Frequencies, amplitudes, phases, and damping factors are estimated by applying a generalization of the monodimensional Prony's method to spatial data. This procedure finds the desired quantities after an autoregressive model fitting to the data, a polynomial rooting, and a least-squares problem solution. The autoregressive models involved have a particular nature that simplifies the analysis. In fact, their characteristic polynomial factors into two parts so that many of their properties can be easily determined. Quick estimates of the parameters computed are found by using standard one-dimensional autoregressive estimation methods. An iterative procedure for refining the autoregressive parameters estimates which gives rise to better frequency estimates is also discussed. Some simulation results are reported. >
33 citations
TL;DR: A test based on the Euclidean distance between the autocorrelation functions of two series and a Monte Carlo study with the size and the power of the proposed test are studied.
32 citations
23 May 1989
TL;DR: The present technique performed better than spectral subtraction in noise immunity experiments on the IBM isolated word speech-recognition system, although at the expense of additional computational requirements.
Abstract: A novel algorithm is presented for the estimation of a signal in noise. The distortion criterion used is based on the distance between log spectra. In many signal-processing applications, such as speech recognition, log spectra are much closer to the parameters used in a discriminator than power spectra. Therefore, it is believed that this spectral estimation technique should lead to better results than previously developed techniques such as spectral subtraction. The present technique performed better than spectral subtraction in noise immunity experiments on the IBM isolated word speech-recognition system, although at the expense of additional computational requirements. >
32 citations
TL;DR: In this article, a technique for stable calculation of the orthogonal tapers from the basic defining equation is described, which is difficult due to the instability of the calculations and the eigenproblem is poorly conditioned.
Abstract: Spectral estimation using a set of orthogonal tapers is becoming widely used and appreciated in scientific research. It produces direct spectral estimates with more than 2 df at each Fourier frequency, resulting in spectral estimators with reduced variance. Computation of the orthogonal tapers from the basic defining equation is difficult, however, due to the instability of the calculations—the eigenproblem is very poorly conditioned. In this article the severe numerical instability problems are illustrated and then a technique for stable calculation of the tapers—namely, inverse iteration—is described. Each iteration involves the solution of a matrix equation. Because the matrix has Toeplitz form, the Levinson recursions are used to rapidly solve the matrix equation. FORTRAN code for this method is available through the Statlib archive. An alternative stable method is also briefly reviewed.
32 citations