@InProceedings{8553393,
  author = {E. Cuoco and M. Razzano and A. Utina},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Wavelet-Based Classification of Transient Signals for Gravitational Wave Detectors},
  year = {2018},
  pages = {2648-2652},
  abstract = {The detection of gravitational waves opened a new window on the cosmos. The Advanced LIGO and Advanced Virgo interferometers will probe a larger volume of Universe and discover new gravitational wave emitters. Characterizing these detectors is of primary importance in order to recognize the main sources of noise and optimize the sensitivity of the searches. Glitches are transient noise events that can impact the data quality of the interferometers and their classification is an important task for detector characterization. In this paper we present a classification method for short transient signals based on a Wavelet decomposition and de-noising and a classification of the extracted features based on XGBoost algorithm. Although the results show the accuracy is lower than that obtained with the use of deep learning, this method which extracts features while detecting signals in real time, can be configured as a fast classification system.},
  keywords = {gravitational wave detectors;gravitational waves;light interferometers;wavelet transforms;Advanced LIGO;Advanced Virgo interferometers;XGBoost algorithm;cosmos;gravitational wave detectors;Wavelet-based classification;fast classification system;short transient signals;classification method;detector characterization;data quality;transient noise events;gravitational wave emitters;Wavelet transforms;Transient analysis;Detectors;Interferometers;Pipelines;Sensitivity;Feature extraction;signal processing;wavelet decomposition;machine learning classification},
  doi = {10.23919/EUSIPCO.2018.8553393},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570436751.pdf},
}

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Characterizing these detectors is of primary importance in order to recognize the main sources of noise and optimize the sensitivity of the searches. Glitches are transient noise events that can impact the data quality of the interferometers and their classification is an important task for detector characterization. In this paper we present a classification method for short transient signals based on a Wavelet decomposition and de-noising and a classification of the extracted features based on XGBoost algorithm. 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Cuoco and M. Razzano and A. Utina},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Wavelet-Based Classification of Transient Signals for Gravitational Wave Detectors},\n year = {2018},\n pages = {2648-2652},\n abstract = {The detection of gravitational waves opened a new window on the cosmos. The Advanced LIGO and Advanced Virgo interferometers will probe a larger volume of Universe and discover new gravitational wave emitters. Characterizing these detectors is of primary importance in order to recognize the main sources of noise and optimize the sensitivity of the searches. Glitches are transient noise events that can impact the data quality of the interferometers and their classification is an important task for detector characterization. In this paper we present a classification method for short transient signals based on a Wavelet decomposition and de-noising and a classification of the extracted features based on XGBoost algorithm. 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