On a chirplet transform-based method applied to separating and counting wolf howls

Dugnol, B.; Fernández, Carlos; Galiano, Gonzalo; Velasco, Julián

doi:10.1016/j.sigpro.2008.01.018

Cited by 33 publications

(14 citation statements)

References 15 publications

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“…First attempts to denoising and enhancing of spectrogram images were made via local differential filters [6,7,9] based on corresponding well established methods, see [1,5,16], in which the filtering process of a intensity image S : Ω → [0, 1] at x ∈ Ω is based only on the intensities in a neighborhood of x. Although the resulting denoised spectrogram greatly improves the IF estimation of the signal, both computational time and low energy harmonic removing were drawbacks which motivated different approaches, see [8].…”

Section: Mathematical Frameworkmentioning

confidence: 99%

On a nonlocal spectrogram for denoising one-dimensional signals

Galiano

Velasco

2014

Applied Mathematics and Computation

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In previous works, we investigated the use of local filters based on partial differential equations (PDE) to denoise one-dimensional signals through the image processing of timefrequency representations, such as the spectrogram. In this image denoising algorithms, the particularity of the image was hardly taken into account. We turn, in this paper, to study the performance of non-local filters, like Neighborhood or Yaroslavsky filters, in the same problem. We show that, for certain iterative schemes involving the Neighborhood filter, the computational time is drastically reduced with respect to Yaroslavsky or nonlinear PDE based filters, while the outputs of the filtering processes are similar. This is heuristically justified by the connection between the (fast) Neighborhood filter applied to a spectrogram and the corresponding Nonlocal Means filter (accurate) applied to the Wigner-Ville distribution of the signal. This correspondence holds only for time-frequency representations of one-dimensional signals, not to usual images, and in this sense the particularity of the image is exploited. We compare though a series of experiments on synthetic and biomedical signals the performance of local and non-local filters.

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Section: Mathematical Frameworkmentioning

confidence: 99%

On a nonlocal spectrogram for denoising one-dimensional signals

Galiano

Velasco

2014

Applied Mathematics and Computation

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“…The "matching-pursuit" and "ridge-pursuit" algorithms have been used by a number of authors; for details see e.g. [1,4,7,12,19]. These typically require complicated multi-dimensional searches to obtain the chirp parameters and they also demand complete a-priori dictionary of chirps.…”

Section: The Objective Of the Papermentioning

confidence: 99%

“…The main drawback in the setup of chirp decomposition techniques is the weight of the computational effort required: according to the literature, most of the algorithms rely on matching pursuit techniques [12] where one first considers a rather complete dictionary of chirps in order to find iteratively the ones matching the signal as best as possible (see [1,4,7,9,10,11,14] and the recent paper [2] motivated by gravitational waves detection). However, in a very recent paper, Greenberg and co-authors [6] proposed a completely different approach where one gets rid of the dictionary and constructs the chirp approximation by means of a simple and easy-to-implement procedure.…”

Section: Introductionmentioning

confidence: 99%

Chirplet Approximation of Band-Limited, Real Signals Made Easy

Greenberg¹,

Gosse²

2009

SIAM J. Sci. Comput.

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In this paper we present algorithms for approximating real bandlimited signals by multiple Gaussian Chirps. These algorithms do not rely on matching pursuit ideas. They are hierarchial and, at each stage, the number of terms in a given approximation depends only on the number of positive-valued maxima and negative-valued minima of a signed amplitude function characterizing part of the signal. Like the algorithms used in [6] and unlike previous methods, our chirplet approximations require neither a complete dictionary of chirps nor complicated multi-dimensional searches to obtain suitable choices of chirp parameters.

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“…Only one non-linear chirp is detected in the signal, consequently this approach is inappropriate for the multiple chirps of model (1). A more general chirplet chain based on a parametric model is proposed by Dugnal et al [7], which uses local maxima to start chirplet chains, and a single criterion based on the smoothness of the frequency modulation. Other approaches to detec and estimate the parameters of chirps exist, based for example on higher order moments [8] or evolutionary algorithm [9].…”

Section: Introductionmentioning

confidence: 99%

Sparse Detection in the Chirplet Transform: Application to FMCW Radar Signals

Millioz

Davies

2012

IEEE Trans. Signal Process.

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Abstract-This paper aims to detect and characterise a signal coming from Frequency Modulation Continuous Wave radars. The radar signals are made of piecewise linear frequency modulations. The Maximum Chirplet Transform, a simplification of the chirplet transform is proposed. A detection of the relevant Maximum Chirplets is proposed based on iterative masking, an iterative detection followed by window subtraction that does not require the recomputation of the spectrum. This detection is designed to provide a sparse subset of Maximum Chirplet coefficients. The chirplets are then gathered into linear chirps whose starting time, length, and chirprate are estimated. These chirps are then gathered again back into the different Frequency Modulation Continuous Wave signals, ready to be classified. An illustration is provided on synthetic data.

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On a chirplet transform-based method applied to separating and counting wolf howls

Cited by 33 publications

References 15 publications

On a nonlocal spectrogram for denoising one-dimensional signals

On a nonlocal spectrogram for denoising one-dimensional signals

Chirplet Approximation of Band-Limited, Real Signals Made Easy

Sparse Detection in the Chirplet Transform: Application to FMCW Radar Signals

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