Multiple window time-varying spectral analysis

Cakrak, F.; Loughlin, Patrick J.

doi:10.1109/78.902129

Cited by 42 publications

(20 citation statements)

References 19 publications

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“…The spectrogram, or squared-magnitude of the sliding-window short-time Fourier transform (STFT), is the principal tool used to estimate the time-dependent spectral energy density in many applications. While there are many ways and criteria guiding the selection of the spectrogram windows [15][16][17][18][19][20][21], we deal, without loss of generality, with those which are obtained as eigenvectors of desirable timefrequency distribution (TFD) kernels, an approach known as spectrogram decompositions of TFDs [22,23].…”

Section: Introductionmentioning

confidence: 99%

Time-frequency signature reconstruction from random observations using multiple measurement vectors

Amin

Zhang

Jokanović

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A new approach for sparse nonstationary signal reconstruction based on multiple windows is introduced. Signals which are localizable in the time-frequency (TF) domain give rise to sparsity in the same domain. When combined, sparse reconstructions, applied to randomly sampled data and corresponding to different selected windows, provide enhanced TF signature estimation. Among possible orthogonal windows, we consider those which characterize the eigen-decomposition of reducedinterference quadratic time-frequency distribution kernels. The highly overlapping TF support of the windows' full-data spectrograms inspires the use of the multiple measurement vectors, in lieu of individual windowed signal recovery. It is shown that the proposed approach outperforms other reconstruction methods when only a single window is applied and is superior to reduced interference time-frequency distributions of random observations.

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Section: Introductionmentioning

confidence: 99%

Time-frequency signature reconstruction from random observations using multiple measurement vectors

Amin

Zhang

Jokanović

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Conventionally, the basis functions have been chosen to be Chebyshev and Legendre polynomials, prolate spheroidal sequences which are the best approximation to bandlimited functions [2], [4], [12]- [13] and wavelet basis that have a distinctive property of multi-resolution in both the time and frequency domains [3], [14]- [15]. Basis expansion methods have been widely applied to solve various engineering problems.…”

Section: Introductionmentioning

confidence: 99%

Time-varying model identification for time–frequency feature extraction from EEG data

Wei

Billings

et al. 2011

Journal of Neuroscience Methods

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A novel modelling scheme that can be used to estimate and track time-varying properties of nonstationary signals is investigated. This scheme is based on a class of time-varying AutoRegressive with an eXogenous input (ARX) models where the associated time-varying parameters are represented by multi-wavelet basis functions. The orthogonal least square (OLS) algorithm is then applied to refine the model parameter estimates of the time-varying ARX model. The main features of the multi-wavelet approach is that it enables smooth trends to be tracked but also to capture sharp changes in the time-varying process parameters. Simulation studies and applications to real EEG data show that the proposed algorithm can provide important transient information on the inherent dynamics of nonstationary processes.

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“…In this respect and for a sake of comparison, we first consider in Fig. 6 the case already discussed in [4] and [5], with both a (nonlinear) chirp component and a (bandpass) time-varying noise. Concerning spectrum estimation, the effectiveness of the approach is clearly supported by this example which evidences the good tradeoff achieved between TF localization along the chirp and smoothness within the (time-varying) frequency band of the noise.…”

Section: Examplesmentioning

confidence: 99%

“…Turning to the estimation issue in a statistical sense, different attempts have been made to take advantage of the idea of multitapering, pioneered by Thomson in a stationary setting [13], and thanks to which an improved statistical stability can be obtained without a time-averaging step. Numerous extensions of multitaper techniques to nonstationary situations have been proposed; see, e.g., [5], [10] and, for a more comprehensive covering of the topics and of the literature, [4], [15], and the references therein. When extended in a direct way, the "classical" method of multitapering suffers, however, still from the TF localization tradeoff previously mentioned.…”

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confidence: 99%

Multitaper Time-Frequency Reassignment for Nonstationary Spectrum Estimation and Chirp Enhancement

Xiao

Flandrin

2007

IEEE Trans. Signal Process.

129

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Abstract-A method is proposed for obtaining time-frequency distributions of chirp signals embedded in nonstationary noise, with the two-fold objective of a sharp localization for the chirp components and a reduced level of statistical fluctuations for the noise. The technique consists in combining time-frequency reassignment with multitapering, and two variations are proposed. The first one, primarily aimed at nonstationary spectrum estimation, is based on sums of estimates with different tapers, whereas the second one makes use of differences between the same estimates for the sake of chirp enhancement. The principle of the technique is outlined, its implementation based on Hermite functions is justified and discussed, and some examples are provided for supporting the efficiency of the approach, both qualitatively and quantitatively.

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Multiple window time-varying spectral analysis

Cited by 42 publications

References 19 publications

Time-frequency signature reconstruction from random observations using multiple measurement vectors

Time-frequency signature reconstruction from random observations using multiple measurement vectors

Time-varying model identification for time–frequency feature extraction from EEG data

Multitaper Time-Frequency Reassignment for Nonstationary Spectrum Estimation and Chirp Enhancement

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