A new method for the numerical analysis of non-stationary signals

Kodera, Kunihiko; Villedary, C. de; Gendrin, R.

doi:10.1016/0031-9201(76)90044-3

Cited by 223 publications

(122 citation statements)

References 4 publications

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“…For non-stationary signals the reassigned spectrogram (ReSpect) can improve the readability of the time-frequency representation [1], [2]. The concentration of a component is increased by reassigning mass to the centre of gravity, squeezing the signal terms to be more localised, while crossterms are reduced by a smoothing of the specific distribution.…”

Section: Introductionmentioning

confidence: 99%

The scaled reassigned spectrogram adapted for detection and localisation of transient signals

Reinhold

Starkhammar

Sandsten

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The reassigned spectrogram can be used to improve the readability of a time-frequency representation of a nonstationary and multi-component signal. However for transient signals the reassignment needs to be adapted in order to achieve good localisation of the signal components. One approach is to scale the reassignment. This paper shows that by adapting the shape of the time window used with the spectrogram and by scaling the reassignment, perfect localisation can be achieved for a transient signal component. It is also shown that without matching the shape of the window, perfect localisation is not achieved. This is used to both identify the time-frequency centres of components in a multi-component signal, and to detect the shapes of the signal components. The scaled reassigned spectrogram with the matching shape window is shown to be able to resolve close components and works well for multi-components signals with noise. An echolocation signal from a beluga whale (Delphinapterus leucas) provides an example of how the method performs on a measured signal.

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Section: Introductionmentioning

confidence: 99%

The scaled reassigned spectrogram adapted for detection and localisation of transient signals

Reinhold

Starkhammar

Sandsten

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…We shall refer to these methods generally as temporal representations. Temporal representations include reassigned spectrograms (Kodera et al, 1976(Kodera et al, , 1978Gardner and Magnasco, 2006;Fulop and Fitz, 2006), the ensemble interval histogram ( EIH Ghitza, 1988;Chandrasekhar and Sreenivas, 2005), zerocrossings with peak amplitudes (ZCPA; Kim et al, 1999;Haque et al, 2007), in-band synchrony (Cooke, 1991(Cooke, /1993Seneff, 1988), sinusoidal representations (McAuley and Quatieri, 1986), and fine-structure spectrography (Dajani et al, 2005). The mammalian ear itself also belongs to this class of system (Pickles, 2012), and it can be modelled as a cochlear filtering stage followed by non-linear transforms on the fine structure in band-pass signals (e.g., Sumner et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Utilising temporal signal features in adverse noise conditions: Detection, estimation, and the reassigned spectrogram

Mill

Brown

2016

The Journal of the Acoustical Society of America

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Utilising temporal signal features in adverse noise conditions 1 AbstractVisual displays in passive sonar based on the Fourier spectrogram are underpinned by detection models that rely on signal and noise power statistics. Time-frequency representations specialised for sparse signals achieve a sharper signal representation, either by reassigning signal energy based on temporal structure or by conveying temporal structure directly. However, temporal representations involve nonlinear transformations that make it difficult to reason about how they respond to additive noise. This article analyses the effect of noise on temporal fine structure measurements such as zero crossings and instantaneous frequency. Detectors that rely on zero crossing intervals, intervals and peak amplitudes, and instantaneous frequency measurements are developed, and evaluated for the detection of a sinusoid in Gaussian noise, using the power detector as a baseline. Detectors that rely on fine structure outperform the power detector under certain circumstances; and detectors that rely on both fine structure and power measurements are superior. Reassigned spectrograms assume that the statistics used to reassign energy are reliable, but the derivation of the fine structure detectors indicates the opposite. The article closes by proposing and demonstrating the concept of a doubly-reassigned spectrogram, wherein temporal measurements are reassigned according to a statistical model of the noise background.

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“…The principle of this method is to move each value of the spectrogram from the point (t, ω) where it is computed to another point (t ′ , ω ′ ) which is more representative of the localization of the signal energy. In [5,6], this point was chosen as the centroid (t,ω) of the signal energy in the neighborhood of (t, ω). As a consequence of this definition, there is no parameter allowing to adjust the concentration of the signal energy to the user's needs, so as to find a trade-off between sparsity and information loss.…”

Section: Introductionmentioning

confidence: 99%

“…then the classical reassignment operators can be derived from the partial derivatives of the phase of the STFT [5,6]:…”

Section: Ifmentioning

confidence: 99%

Making reassignment adjustable: The Levenberg-Marquardt approach

Auger

Chassande–Mottin

Flandrin

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A new method for the numerical analysis of non-stationary signals

Cited by 223 publications

References 4 publications

The scaled reassigned spectrogram adapted for detection and localisation of transient signals

The scaled reassigned spectrogram adapted for detection and localisation of transient signals

Utilising temporal signal features in adverse noise conditions: Detection, estimation, and the reassigned spectrogram

Making reassignment adjustable: The Levenberg-Marquardt approach

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