Best basis selection using sparsity driven multi-family wavelet transform

Cosentino, Romain; Balestriero, Randall; Aazhang, Behnaam

doi:10.1109/globalsip.2016.7905842

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Other successful deep‐learning classifiers inspired by deep scattering networks are presented in Balestriero et al. (2018) and Cosentino and Aazhang (2020).…”

Section: Methodsmentioning

confidence: 99%

“…The authors of Seydoux et al (2020) have brought that representation into seismology and showed that small precursory signals of a landslide could be detected and classified in an unsupervised fashion. Other successful deep-learning classifiers inspired by deep scattering networks are presented in Balestriero et al (2018) and Cosentino and Aazhang (2020).…”

Section: Finding An Appropriate Representation Of Seismograms: the De...mentioning

confidence: 99%

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Hierarchical Exploration of Continuous Seismograms With Unsupervised Learning

et al. 2022

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Continuous seismograms contain a wealth of information with a large variety of signals with different origin. Identifying these signals is a crucial step in understanding physical geological objects. We propose a strategy to identify classes of signals in continuous single‐station seismograms in an unsupervised fashion. Our strategy relies on extracting meaningful waveform features based on a deep scattering network combined with an independent component analysis. Based on the extracted features, agglomerative clustering then groups these waveforms in a hierarchical fashion and reveals the process of clustering in a dendrogram. We use the dendrogram to explore the seismic data and identify different classes of signals. To test our strategy, we investigate a two‐day‐long seismogram collected in the vicinity of the North Anatolian Fault, Turkey. We analyze the automatically inferred clusters' occurrence rate, spectral characteristics, cluster size, and waveform and envelope characteristics. At a low level in the cluster hierarchy, we obtain three clusters related to anthropogenic and ambient seismic noise and one cluster related to earthquake activity. At a high level in the cluster hierarchy, we identify a seismic burst that includes around 200 events with similar waveforms and high‐frequent signals with correlating envelopes and an anthropogenic origin. The application shows that the cluster hierarchy helps to identify particular families of signals and to extract subclusters for further analysis. This is valuable when certain types of signals, such as earthquakes, are under‐represented in the data. The proposed method may also successfully discover new types of signals since it is entirely data‐driven.

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“…Other successful deep‐learning classifiers inspired by deep scattering networks are presented in Balestriero et al. (2018) and Cosentino and Aazhang (2020).…”

Section: Methodsmentioning

confidence: 99%

Section: Finding An Appropriate Representation Of Seismograms: the De...mentioning

confidence: 99%

Hierarchical Exploration of Continuous Seismograms With Unsupervised Learning

et al. 2022

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“…In most cases, wavelets can be used for three different tasks. First, for compression purposes: in fact, it has been demonstrated that wavelet transforms provide sparse representations particularly adapted for compression of natural signals with non-stationary components [14,33]. Secondly, for denoising purposes.…”

Section: Introductionmentioning

confidence: 99%

Overcomplete Frame Thresholding for Acoustic Scene Analysis

Cosentino,

Balestriero,

Baraniuk

et al. 2017

Preprint

Self Cite

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In this work, we derive a generic overcomplete frame thresholding scheme based on risk minimization. Overcomplete frames being favored for analysis tasks such as classification, regression or anomaly detection, we provide a way to leverage those optimal representations in real-world applications through the use of thresholding. We validate the method on a large scale bird activity detection task via the scattering network architecture performed by means of continuous wavelets, known for being an adequate dictionary in audio environments.

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Wavelet Learning by Adaptive Hermite Cubic Splines applied to Bioacoustic Chirps

Balestriero

Glotin

2019

OCEANS 2019 - Marseille

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Best basis selection using sparsity driven multi-family wavelet transform

Cited by 3 publications

References 19 publications

Hierarchical Exploration of Continuous Seismograms With Unsupervised Learning

Hierarchical Exploration of Continuous Seismograms With Unsupervised Learning

Overcomplete Frame Thresholding for Acoustic Scene Analysis

Wavelet Learning by Adaptive Hermite Cubic Splines applied to Bioacoustic Chirps

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