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cats

Cluster Analysis of Trimmed Spectrograms: framework for detection and denoising of sparse signals in time-frequency domain.

https://github.com/sgrubas/cats

Science Score: 67.0%

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    CITATION.cff file
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    DOI references
    Found 2 DOI reference(s) in README
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    Links to: zenodo.org
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    Scientific vocabulary similarity
    Low similarity (11.5%) to scientific vocabulary

Keywords

denoising earthquake-detection signal-detection sparse-representation
Last synced: 6 months ago · JSON representation ·

Repository

Cluster Analysis of Trimmed Spectrograms: framework for detection and denoising of sparse signals in time-frequency domain.

Basic Info
  • Host: GitHub
  • Owner: sgrubas
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 79.1 MB
Statistics
  • Stars: 11
  • Watchers: 4
  • Forks: 1
  • Open Issues: 0
  • Releases: 2
Topics
denoising earthquake-detection signal-detection sparse-representation
Created about 3 years ago · Last pushed 6 months ago
Metadata Files
Readme License Citation

README.md

Cluster Analysis of Trimmed Spectrograms (CATS)

DOI

CATS is a signal processing technique and framework for detecting and denoising sparse signals in the time-frequency domain. Particularly, very useful for processing earthquakes. This work is still in progress, and the package is under active development. Soon, here will be links to our papers/preprints.

Key features of CATS

  • Versatile. Any sparse signals in the time-frequency domain can be localized by CATS.
  • Flexible. Fast detection with STFT or more accurate denoising with CWT.
  • Fast and accurate. Here will be links to our papers showing this.
  • Comprehensive quality control.
    • Autotunable parameters with direct physical interpretation.
    • Easy visualization of all intermediate workflow steps.
    • Collected cluster statistics allow for fine-grained QC and classification of signals.

Installation

To install the package: 1. Short way: pip install git+https://github.com/sgrubas/cats.git 2. Other way: 1. Clone repository: git clone https://github.com/sgrubas/cats.git 2. Open the cats directory: cd cats 3. Install: 1) pip install . or 2) pip install -e . (editable mode) 3. To update: pip install -U git+https://github.com/sgrubas/cats.git

Dependencies

The package was tested on Python 3.9. See other dependencies in requirements.txt.

Tutorials

Demos:

Signal detection with CATSDetector

Signal denoising with CATSDenoiser and CATSDenoiserCWT

Citation

If you find CATS useful for your research, please cite the repository (CITATION.bib).

Authors

  • Serafim Grubas (serafimgrubas@gmail.com, grubas@ualberta.ca)
  • Mirko van der Baan

Owner

  • Name: Serafim Grubas
  • Login: sgrubas
  • Kind: user
  • Location: Edmonton
  • Company: University of Alberta

PhD student at University of Alberta

Citation (CITATION.bib) 

@software{grubas2025cats,
  author       = {Serafim Grubas},
  title        = {CATS: Cluster Analysis of Trimmed Spectrograms},
  month        = jun,
  year         = {2025},
  publisher    = {Zenodo},
  journal      = {GitHub},
  version      = {v0.3.0},
  doi          = {10.5281/zenodo.15627707},
  url          = {https://doi.org/10.5281/zenodo.15627707}
}

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