On PDE-based spectrogram image restoration. Application to wolf chorus noise reduction and comparison with other algorithms

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

doi:10.1007/978-0-387-72500-0_1

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…As shown in (B. Dugnol, 2007a;B. Dugnol, 2007d), when a strong noise is added to a clean signal better results are obtained for approximating the clean spectrogram if we use a noise reduction edge enhancement PDE based algorithm than if we simply threshold the image spectrogram.…”

Section: Signal Enhancementmentioning

confidence: 99%

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Noise Reduction and Voice Separation Algorithms Applied to Wolf Population Counting

Dugnol¹,

Fernández²,

Galiano³

et al. 2008

Proceedings of the First International Conference on Bio-Inspired Systems and Signal Processing

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We use signal and image theory based algorithms to produce estimations of the number of wolves emitting howls or barks in a given field recording as an individuals counting alternative to the traditional trace collecting methodologies. We proceed in two steps. Firstly, we clean and enhance the signal by using PDE based image processing algorithms applied to the signal spectrogram. Secondly, assuming that the wolves chorus may be modelled as an addition of nonlinear chirps, we use the quadratic energy distribution corresponding to the Chirplet Transform of the signal to produce estimates of the corresponding instantaneous frequencies, chirprates and amplitudes at each instant of the recording. We finally establish suitable criteria to decide how such estimates are connected in time.

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Section: Signal Enhancementmentioning

confidence: 99%

“…In previous works (B. Dugnol, 2007a;B. Dugnol, 2007d), we investigated the noise reduction and edge (IF lines) enhancement on the spectrogram image by a PDE-based image processing algorithm.…”

Section: Signal Enhancementmentioning

confidence: 99%

Noise Reduction and Voice Separation Algorithms Applied to Wolf Population Counting

Dugnol¹,

Fernández²,

Galiano³

et al. 2008

Proceedings of the First International Conference on Bio-Inspired Systems and Signal Processing

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“…For methods for denoising sounds using PDE see, e.g., [7], [8], [2] and [9]. For techniques for denoising images see [10], [11], [12] and [13].…”

Section: Denoising With Pdementioning

confidence: 99%

PDE-SVD based audio denoising

Baravdish

Evangelista

Svensson

et al. 2012

2012 5th International Symposium on Communications, Control and Signal Processing

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In this paper we present a new method for denoising audio signals. The method is based on the Singular Value Decomposition (SVD) of the frame matrix representing the signal in the Overlap Add decomposition. Denoising is performed by modifying both the singular values, using a tapering model, and the singular vectors of the representation, using a nonlinear PDE method. The performance of the method is evaluated and compared with denoising obtained by filtering.

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