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Time–frequency analysis

About: Time–frequency analysis is a research topic. Over the lifetime, 5407 publications have been published within this topic receiving 104346 citations.


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Proceedings ArticleDOI
21 Oct 2001
TL;DR: Qualitative results indicate the potential of the proposed reduced multi-Gabor systems to yield a salient representation of typical audio signals while at the same time reducing computational costs as compared to a full multiresolution decomposition.
Abstract: We consider the construction of multiresolution Gabor dictionaries appropriate for audio signal analysis. Motivated by a desire for parsimony and efficiency, we propose and formalise the idea of reduced multi-Gabor systems, showing that they constitute a frame for L/sup 2/(R) and other Hilbert spaces of interest. In order to demonstrate the practicality of such a scheme, we apply it to the atomic decomposition of music and speech signals observed in noise. Qualitative results indicate the potential of this method to yield a salient representation of typical audio signals while at the same time reducing computational costs as compared to a full multiresolution decomposition.

37 citations

Journal ArticleDOI
TL;DR: A new local polynomial modeling method for identification of time-varying autoregressive (TVAR) models and applies it to time-frequency analysis (TFA) of event-related electroencephalogram (ER-EEG).
Abstract: This paper proposes a new local polynomial modeling (LPM) method for identification of time-varying autoregressive (TVAR) models and applies it to time-frequency analysis (TFA) of event-related electroencephalogram (ER-EEG). The LPM method models the TVAR coefficients locally by polynomials and estimates the polynomial coefficients using weighted least-squares with a window having a certain bandwidth. A data-driven variable bandwidth selection method is developed to determine the optimal bandwidth that minimizes the mean squared error. The resultant time-varying power spectral density estimation of the signal is capable of achieving both high time resolution and high frequency resolution in the time-frequency domain, making it a powerful TFA technique for nonstationary biomedical signals like ER-EEG. Experimental results on synthesized signals and real EEG data show that the LPM method can achieve a more accurate and complete time-frequency representation of the signal.

37 citations

Journal ArticleDOI
TL;DR: The results reveal that the proposed rule-based ST and AdaBoost based method performs better than the other methods viz., SVM and Decision Tree (DT), under varied noise conditions as well as under varied amount of data used for training.

37 citations

Journal ArticleDOI
TL;DR: In this article, a multichannel vibration data processing method for local damage detection in gearboxes is presented. The method is a combination of time-frequency representation and principal component analysis (PCA) applied not to the raw time series but to each slice (along the time) from its spectrogram.
Abstract: A multichannel vibration data processing method in the context of local damage detection in gearboxes is presented in this paper. The purpose of the approach is to achieve more reliable information about local damage by using several channels in comparison to results obtained by single channel vibration analysis. The method is a combination of time-frequency representation and Principal Component Analysis (PCA) applied not to the raw time series but to each slice (along the time) from its spectrogram. Finally, we create a new time-frequency map which aggregated clearly indicates presence of the damage. Details and properties of this procedure are described in this paper, along with comparison to single-channel results. We refer to autocorrelation function of the new aggregated time frequency map (1D signal) or simple spectrum (that might be somehow linked to classical envelope analysis). The results are very convincing – cyclic impulses associated with local damage might be clearly detected. In order to validate our method, we used a model of vibration data from heavy duty gearbox exploited in mining industry.

37 citations

Journal ArticleDOI
TL;DR: A novel WBI suppression method using iterative adaptive approach (IAA) and orthogonal subspace projection (OSP) method is proposed for synthetic aperture radar (SAR) and obtains time-frequency distribution (TFD) with 2-D high resolution and no cross-terms.
Abstract: Under the condition of wideband interference (WBI) with the characteristics of good time–frequency concentration but high nonstationarity, the limited time width of the window function in short-time Fourier transform (STFT) causes limited instantaneous frequency resolution and leads to great performance degradation of the conventional WBI suppression algorithm based on time–frequency filtering (TFF) method. A novel WBI suppression method using iterative adaptive approach (IAA) and orthogonal subspace projection (OSP) method is proposed for synthetic aperture radar (SAR). Dispensing with parametric search and model order estimation, the proposed method improves the instantaneous frequency resolution in STFT by means of the IAA method and filters the WBI based on the OSP method, meanwhile, obtains time–frequency distribution (TFD) with 2-D high resolution and no cross-terms. Both the simulation and experimental results are provided to illustrate the performance of the proposed method.

37 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023164
2022338
2021253
2020229
2019261
2018320