Pattern analysis with two-dimensional spectral localisation: Applications of two-dimensional S transforms

Mansinha, L.; Stockwell, R. G.; Lowe, R. P.

doi:10.1016/s0378-4371(96)00487-6

Cited by 156 publications

(72 citation statements)

References 5 publications

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“…A key aspect of the Gaussian window in Eq. (3) is that the standard deviation is scaled for each frequency as σ = c |f | , where c is a scaling parameter usually set to 1 (Mansinha et al, 1997a). This window is often referred to as the "voice Gaussian", which provides localisation of a specific frequency "voice" (Stockwell, 1999).…”

Section: The Stockwell Transformmentioning

confidence: 99%

“…The 2DST is introduced and well described by Mansinha et al (1997a) and Mansinha et al (1997b), who demonstrated its promise for pattern analysis. It has since been discussed and applied in a variety of fields (Liu and Wong, 2007;Kocahan et al, 2008;Liu, 2009;Barry et al, 2012) but to our knowledge it has yet to be used for geophysical data analysis in the atmospheric sciences, despite the wide use of the 1-D form.…”

Section: The Two-dimensional Stockwell Transformmentioning

confidence: 99%

“…This is so that the spatial integral of the Stockwell transform is equal to the Fourier transform H (k x , k y ) (Mansinha et al, 1997a), namely…”

Section: The Elliptic-bessel Windowmentioning

confidence: 99%

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A two-dimensional Stockwell transform for gravity wave analysis of AIRS measurements

et al. 2016

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Abstract. Gravity waves (GWs) play a crucial role in the dynamics of the earth's atmosphere. These waves couple lower, middle and upper atmospheric layers by transporting and depositing energy and momentum from their sources to great heights. The accurate parameterisation of GW momentum flux is of key importance to general circulation models but requires accurate measurement of GW properties, which has proved challenging. For more than a decade, the nadir-viewing Atmospheric Infrared Sounder (AIRS) aboard NASA's Aqua satellite has made global, two-dimensional (2-D) measurements of stratospheric radiances in which GWs can be detected. However, one problem with current one-dimensional methods for GW analysis of these data is that they can introduce significant unwanted biases. Here, we present a new analysis method that resolves this problem. Our method uses a 2-D Stockwell transform (2DST) to measure GW amplitudes, horizontal wavelengths and directions of propagation using both the along-track and cross-track dimensions simultaneously. We first test our new method and demonstrate that it can accurately measure GW properties in a specified wave field. We then show that by using a new elliptical spectral window in the 2DST, in place of the traditional Gaussian, we can dramatically improve the recovery of wave amplitude over the standard approach. We then use our improved method to measure GW properties and momentum fluxes in AIRS measurements over two regions known to be intense hotspots of GW activity: (i) the Drake Passage/Antarctic Peninsula and (ii) the isolated mountainous island of South Georgia. The significance of our new 2DST method is that it provides more accurate, unbiased and better localised measurements of key GW properties compared to most current methods. The added flexibility offered by the scaling parameter and our new spectral window presented here extend the usefulness of our 2DST method to other areas of geophysical data analysis and beyond.

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Section: The Stockwell Transformmentioning

confidence: 99%

Section: The Two-dimensional Stockwell Transformmentioning

confidence: 99%

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A two-dimensional Stockwell transform for gravity wave analysis of AIRS measurements

et al. 2016

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“…The Stockwell Transforms proposed in 1996 [11,17,18,19,20], gives a full time-frequency decomposition of a signal. The Stockwell transforms (ST) of h(t) is defined as the Fourier transform (FT) of the product between a Gaussian window function, and h(t) .…”

Section: A Stockwell Transformmentioning

confidence: 99%

New Speech Enhancement based on Discrete Orthonormal Stockwell Transform

Saoud¹,

Bousselmi²,

Naser³

et al. 2016

ijacsa

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Abstract-S-transform is an effective time-frequency representation which gives simultaneous frequency and time distribution information alike the wavelet transforms (WT). However, the ST redundantly doubles the dimension of the original data set and the Discrete Orthonormal S-Transform (DOST) can decrease the redundancy of S-transform farther. So, this paper aims to propose a new method to remove additive background noise from noisy speech signal using DOST which supplies a multi-resolution analysis (MRA) spatial-frequency representation of image processing and signal analysis. Hence, the performances of the applied speech enhancement technique have been evaluated objectively and subjectively in comparison with respect to many other methods in four background noises at different SNR levels.

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“…Finally, we applied a 2-D S-transform to the temperature time series to acquire the space localized spectral (wavenumber) information at selected frequencies of interest as identified by the 1-D S-transform [Mansinha et al, 1997b].…”

Section: Time Series and Time-frequency Analysismentioning

confidence: 99%

Spatially variable stage‐driven groundwater‐surface water interaction inferred from time‐frequency analysis of distributed temperature sensing data

Mwakanyamale

Slater

Day‐Lewis

et al. 2012

Geophysical Research Letters

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[1] Characterization of groundwater-surface water exchange is essential for improving understanding of contaminant transport between aquifers and rivers. Fiber-optic distributed temperature sensing (FODTS) provides rich spatiotemporal datasets for quantitative and qualitative analysis of groundwatersurface water exchange. We demonstrate how time-frequency analysis of FODTS and synchronous river stage time series from the Columbia River adjacent to the Hanford 300-Area, Richland, Washington, provides spatial information on the strength of stage-driven exchange of uranium contaminated groundwater in response to subsurface heterogeneity. Although used in previous studies, the stage-temperature correlation coefficient proved an unreliable indicator of the stage-driven forcing on groundwater discharge in the presence of other factors influencing river water temperature. In contrast, S-transform analysis of the stage and FODTS data definitively identifies the spatial distribution of discharge zones and provided information on the dominant forcing periods (≥2 d) of the complex dam operations driving stage fluctuations and hence groundwater-surface water exchange at the 300-Area. Citation: Mwakanyamale, K., L. Slater, F. DayLewis, M. Elwaseif, and C. Johnson (2012), Spatially variable stage-driven groundwater-surface water interaction inferred from time-frequency analysis of distributed temperature sensing data, Geophys. Res. Lett., 39, L06401,

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Pattern analysis with two-dimensional spectral localisation: Applications of two-dimensional S transforms

Cited by 156 publications

References 5 publications

A two-dimensional Stockwell transform for gravity wave analysis of AIRS measurements

A two-dimensional Stockwell transform for gravity wave analysis of AIRS measurements

New Speech Enhancement based on Discrete Orthonormal Stockwell Transform

Spatially variable stage‐driven groundwater‐surface water interaction inferred from time‐frequency analysis of distributed temperature sensing data

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