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Everything about Spectral density estimation
Signal processing technique
In statistical signal processing, the goal of spectral density estimation (SDE) or simply spectral estimation is to estimate the spectral density of a signal from a sequence of time samples of the signal. Intuitively speaking, the spectral density characterizes the frequency content of the signal. One purpose of estimating the spectral density is to detect any periodicities in the data, by observing peaks at the frequencies corresponding to these periodicities. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Spectral density estimationA 5-minute orientation
- Skim a recent paper: On the estimation of the evolutionary power spectral densityInfluential work since 2022
- Pick up a classic book: Neural Information ProcessingThe most-reprinted book on the subject
- Read the landmark paper: Spectral density estimation of stochastic vector processesCited 88 times
- Go sideways: MultitaperA closely related topic
- Go sideways: PeriodogramA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Spectral density estimation of stochastic vector processes
Gaussian estimation of parametric spectral density with unknown pole
Nonparametric Spectral Density Estimation Using Penalized Whittle Likelihood
Automatic Local Smoothing for Spectral Density Estimation
Spectral-density estimation with the Gaussian integral transform
Debiasing Welch’s method for spectral density estimation
Noise Power Spectral Density Estimation Using MaxNSR Blocking Matrix
Bayesian nonparametric spectral density estimation using B-spline priors
🆕 Influential since 2022
On the estimation of the evolutionary power spectral density
A reconstruction method for graph signals based on the power spectral density estimation
Optimally adaptive Bayesian spectral density estimation for stationary and nonstationary processes
Cooperative Spectrum Sensing for Internet of Things Using Modeling of Power-Spectral-Density Estimation Errors
Two-step scalable spectral clustering algorithm using landmarks and probability density estimation
đź“š Books worth reading
Neural Information ProcessingTingwen Huang · 2012
Noise and vibration analysisAnders Brandt · 2010- 📕How to be a quantitative ecologistJason Matthiopoulos · 2011
Engineering Modelling and AnalysisDavid Walker, Michael Leonard · 2009
Information Processing in Medical ImagingJames C. Gee · 2013
Studies in Phase Space Analysis with Applications to PDEsMassimo Cicognani · 2013- 📕On the use of the noncentral chi-square density function for the distribution of helicopter spectral estimatesDonald B. Garber · 1993
Frontiers of blood pressure and heart rate analysisM. Di Rienzo · 1997
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
đź”— Related topics
- Multitaper In signal processing, multitaper analysis is a spectral density estimation technique developed by David J. Thomson. It can estimate the power spectrum…
- Periodogram In signal processing, a periodogram is an estimate of the spectral density of a signal. The term was coined by Arthur Schuster in 1898. Today, the periodogram…
- Cone-shape distribution function The cone-shape distribution function, also called the Zhao–Atlas–Marks distribution or ZAM distribution, is a quadratic time-frequency representation in…
- MUSIC (algorithm) MUSIC (MUltiple SIgnal Classification) is an algorithm used for frequency estimation and radio direction finding. In many practical signal processing problems…
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- Singular spectrum analysis In time series analysis, singular spectrum analysis (SSA) is a nonparametric spectral estimation method. It combines elements of classical time series…
- Reassignment method The method of reassignment is a technique for sharpening a time-frequency representation (e.g. spectrogram or the short-time Fourier transform) by mapping…
- Multidimensional spectral estimation Multidimension spectral estimation is a generalization of spectral estimation, normally formulated for one-dimensional signals, to multidimensional signals…