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.
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17 Feb 1998TL;DR: In this paper, a post-processing method for a speech decoder is proposed, which gives a decoded speech signal in the time domain in order to obtain high frequency resolution from a frequency spectrum having nonharmonic and noise deficiencies.
Abstract: A post-processing method for a speech decoder (1) which gives a decoded speech signal in the time domain in order to obtain high frequency resolution from a frequency spectrum having non-harmonic and noise deficiencies. The method comprises the following steps: a) transforming (21) the decoded time domain signal to a frequency domain signal by means of a high frequency resolution transform (FFT); b) analysing (5) the energy distribution of said frequency domain signal throughout its frequency area (4 kHz) to find the disturbing frequency components and to prioritize such frequency components which are situated in the higher part of the frequency spectrum; c) finding (6) the suppression degree for said disturbing frequency components based on said prioritizing; d) controlling a post-filtering (31) of said transform in dependence of said finding (6); and e) inverse transforming (4) the post-filtered transform in order to obtain a post-filtered decoded speech signal in the time domain.
52 citations
TL;DR: In this paper, the Taylor-Fourier transform (DTFT) is used to identify low-frequency electromechanical modes in power systems, based on the time-frequency analysis of nonlinear signals that arise after a large disturbance.
Abstract: The digital Taylor-Fourier transform (DTFT) is used to identify low-frequency electromechanical modes in power systems. The identification process is based on the time-frequency analysis of nonlinear signals that arise after a large disturbance. The DTFT creates a signal decomposition, from which mono-component signals are extracted by spectral analysis using a filter bank. This analysis is accomplished through sliding-window data, which is updated each sample, yielding estimates of the reconstructed signal and providing information of its instantaneous damping and frequency. Results demonstrate the applicability of the proposition.
52 citations
TL;DR: The numerical simulation and experiment have proved the validity of the multiscale windowed Fourier transform for phase extraction of fringe patterns and makes the extracted phase more precise than other methods.
Abstract: A multiscale windowed Fourier transform for phase extraction of fringe patterns is presented. A local stationary length of signal is used to control the window width of a windowed Fourier transform automatically, which is measured by an instantaneous frequency gradient. The instantaneous frequency of the fringe pattern is obtained by detecting the ridge of the wavelet transform. The numerical simulation and experiment have proved the validity of this method. The combination of the windowed Fourier transform and the wavelet transform makes the extracted phase more precise than other methods.
52 citations
TL;DR: The Auto-SLEX method is presented which is a statistical method that automatically segments the signal into approximately stationary segments using an objective criterion that is based on log energy, and automatically selects the optimal bandwidth of the spectral smoothing window when applied to the intracranial EEG from a patient with temporal lobe epilepsy.
Abstract: In this paper, we apply a new time-frequency spectral estimation method for multichannel data to epileptiform electroencephalography (EEG). The method is based on the smooth localized complex exponentials (SLEX) functions which are time-frequency localized versions of the Fourier functions and, hence, are ideal for analyzing nonstationary signals whose spectral properties evolve over time. The SLEX functions are simultaneously orthogonal and localized in time and frequency because they are obtained by applying a projection operator rather than a window or taper. In this paper, we present the Auto-SLEX method which is a statistical method that 1) computes the periodogram using the SLEX transform, 2) automatically segments the signal into approximately stationary segments using an objective criterion that is based on log energy, and 3) automatically selects the optimal bandwidth of the spectral smoothing window. The method is applied to the intracranial EEG from a patient with temporal lobe epilepsy. This analysis reveals a reduction in average duration of stationarity in preseizure epochs of data compared to baseline. These changes begin up to hours prior to electrical seizure onset in this patient.
52 citations
TL;DR: This letter presents computationally efficient time-updating algorithms of the recent Iterative Adaptive Approach (IAA) spectral estimation technique, which offers a reduction of the necessary computational complexity with at least one order of magnitude.
Abstract: This letter presents computationally efficient time-updating algorithms of the recent Iterative Adaptive Approach (IAA) spectral estimation technique. By exploiting the inherently low displacement rank, together with the development of suitable Gohberg-Semencul (GS) representations, and the use of data dependent trigonometric polynomials, the proposed time-recursive IAA algorithm offers a reduction of the necessary computational complexity with at least one order of magnitude. The resulting complexity can also be reduced further by allowing for approximate solutions. Numerical simulations together with theoretical complexity measures illustrate the achieved performance gain.
52 citations