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.

Selective deconvolution: A new approach to extrapolation and spectral analysis of discrete signals

Abstract: This paper describes a new algorithm useful for extrapolation and Fourier analysis of discrete signals that are given by a relative small number of samples. The extrapolation is based on the assumption that the discrete Fourier spectrum shows dominant spectral lines. Involving only FFT, the iterative algorithm is not restricted to one-dimensional signals but can also be applied to higher-dimensional problems. Additional knowledge on the signal like band-limitedness or positivity can easily be taken into account.

Pitch and spectral estimation of speech based on auditory synchrony model

Abstract: This paper describes a system for processing sonorant regions of speech, motivated by knowledge of the human auditory system. The spectral representation is intended to reflect a proposed model for human auditory processing of speech, which takes advantage of synchrony in the nerve firing patterns to enhance formant peaks. The auditory model is also applied to pitch extraction, and thus a temporal pitch processor is envisioned. The spectrum is derived from the outputs of a set of linear filters with critical bandwidths. Saturation and adaptation are incorporated for each filter independently. Each “spectral” coefficient is determined by weighting the amplitude response at that frequency (corresponding to mean firing rate) by a measure of synchrony to the center frequency of the filter. Pitch is derived from a waveform generated by adding the (weighted) rectified filter outputs across the frequency dimension. The system performance is evaluated by processing of a variety of signals, including natural and synthetic speech, and results are compared with other processing methods and with known psychoacoustical data from these types of stimuli. [Work supported in part by NINCDS and the System Development Foundation.]

Time‐frequency attribute of seismic data using continuous wavelet transform

Abstract: A time-frequency decomposition that can provide higher frequency resolution at lower frequencies and higher time resolution at higher frequencies is desirable for analyzing seismic data. This is because the hydrocarbons in the reservoir are diagnostic at lower frequencies and thin beds can be resolved with enhanced time resolution at higher frequencies. In this paper we present a new method to compute the time-frequency spectrum using wavelet as a window that achieves this objective. Time-frequency spectrum is commonly used to compute various frequency attributes of seismic signal like single frequency, dominant frequency, center frequency and so forth. The conventional approach is to use short time Fourier transform (STFT) to obtain a time-frequency spectrum. Time-frequency resolution in the STFT is limited by the choice of a window length. The proposed time-frequency spectrum using CWT (TFCWT) in this work has the ability to adapt with the frequency content of the signal. The flexibility of not having to choose a window is an advantage of our method. We present two applications of TFCWT to real data sets in this paper. In the first example, we use TFCWT to enhance low frequency shadows caused by hydrocarbon reservoirs. In the second example, we apply the time frequency spectrum in interpreting time slices from a 3D seismic volume in frequency space to identify thin beds below tuning thickness.

Adaptive methods in underwater acoustics

Abstract: 1. Acoustical Background of Signal Processing.- Twenty years of signal processing.- Ambient noise: characteristics of the noise field.- An analytical method to predict the statistical characteristics of ambient shipping noise.- Statistical aspects of sound propagation in the ocean.- Towed array response to ship noise: a near-field propagation problem.- Three-dimensional FFP model of acoustic- and elastic wave propagation in horizontally layered media.- Seismic sensors in underwater acoustics: results from sea-floor measurements in areas of different geology.- Underwater acoustics in the Arctic Ocean.- A new theory for the transmission and reflection coefficient of layered systems.- Second moments of the pressure field near a smooth caustic.- Stochastic systems theory of the scattering of waves from a random medium.- Hydrodynamic flow noise in hydrophones.- Wavevector structure of turbulent wall pressure and its filtering by normal transmission and spatial averaging in sensor arrays.- Target strength and echo structure.- 2. Theoretical and Practical Aspects of Signal Processing.- Time delay estimation.- Time delay estimation in the presence of multipath propagation.- Constrained time delay estimation via zero-crossing methods.- Range and bearing determination: Maximum Likelihood and Kalman-Bucy techniques.- Delay estimation with nonstationary signals and correlated observation noises.- Source parameter estimation by approximate Maximum Likelihood.- On the use of focused horizontal arrays as mode separation and source location devices in ocean acoustics.- High-resolution source-depth estimation in ice covered shallow water.- Spectrum analysis: overview of classical and high resolution spectral estimation.- Application of high resolution spatial processing methods to real data of a fixed array in shallow water.- Experimentation of spatial processing methods.- Simultaneous estimation of spectral lines and of a superimposed continuous spectral density.- Fitting sinusoids to sampled data and correlation sequences.- An extended maximum entropy method for high resolution spatial processing.- Spectral and interspectral analysis of low frequency submarine acoustic signals received on an array of sensors.- Overview of adaptive array processing.- New approaches in the adaptive array processing field.- Multiple detection using eigenvalues when the noise spatial coherence is partially unknown.- Theoretical and experimental comparisons of optimum element, beam and eigen space array processors.- Applications of some statistical measures to spatial signal processing.- The cross correlation matrix in spatial processing.- The effect of bandwidth on the performance of a postbeamformer interference canceller.- A new approach to the design of broadband element space antenna array processors.- On the role of prior information in nonlinear bearing estimation.- Beamforming by the cross-correlation analysis of received spectra.- Applications of adaptive array processing.- Adaptive active sonar reception in shallow water using vertical array outputs covariance matrix eigenvalues: experimental results.- Beamforming with a distorted towed array.- Influence of hydrophone position errors on spatial signal processing algorithms.- A tutorial introduction to nonlinear filtering.- Detection with uncertainty: Nonparametric, robust or adaptive approaches.- The design of optimal processors for arrays with non-Gaussian noise inputs.- Adaptive processing of underwater acoustic signals in non-Gaussian noise environments: I. Detection in the space-time threshold regimes.- Detection and classification phenomena of biological systems.- Detection and recognition of moving or randomly scaled objects.- Iterative algorithms for deconvolution and reconstruction of multidimensional signals from their projections.- 3. Techniques and Applications.- Digital signal processing for sonar.- Optical signal processing.- Beamforming for sonar signals by means of an incoherent acousto-optical processor: experimental results.- Adaptation of fiber optics to hydrophone applications.- Ceramic elastomer composite hydrophone.- Expert systems for ship noise interpretation.- Comparison of the statistical and the expert system approach for the interpretation of ship noise.- Signal processing techniques to analyse and simulate radiated underwater ship propeller noise.- Signal processing in ocean tomography.- New advances towards ocean, acoustics and space integration.- Passive synthetic aperture sonar - an analysis of the beamforming process.- Techniques for measuring backscattering from the sea floor with an array.- AEON - adaptive time-space array processing.- Summaries of Workshops.

Modelling of magnetic data for scaling geology

Abstract: Interpretation of magnetic data can be carried out either in the space or frequency domain. The interpretation in the frequency domain is computationally convenient because convolution becomes multiplication. The frequency domain approach assumes that the magnetic sources distribution has a random and uncorrelated distribution. This approach is modified to include random and fractal distribution of sources on the basis of borehole data. The physical properties of the rocks exhibit scaling behaviour which can be defined as P(k) = Ak−β , where P(k) is the power spectrum as a function of wave number (k), and A and β are the constant and scaling exponent, respectively. A white noise distribution corresponds to β = 0. The high resolution methods of power spectral estimation e.g. maximum entropy method and multi-taper method produce smooth spectra. Therefore, estimation of scaling exponents is more reliable. The values of β are found to be related to the lithology and heterogeneities in the crust. The modelling of magnetic data for scaling distribution of sources leads to an improved method of interpreting the magnetic data known as the scaling spectral method. The method has found applicability in estimating the basement depth, Curie depth and filtering of magnetic data.