@inproceedings{44ddba93dd2744aa8445261df69ba7c8,
title = "Deep multi-view models for glitch classification",
abstract = "Non-cosmic, non-Gaussian disturbances known as 'glitches', show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO. In this paper, we propose a deep multi-view convolutional neural network to classify glitches automatically. The primary purpose of classifying glitches is to understand their characteristics and origin, which facilitates their removal from the data or from the detector entirely. We visualize glitches as spectrograms and leverage the state-of-the-art image classification techniques in our model. The suggested classifier is a multi-view deep neural network that exploits four different views for classification. The experimental results demonstrate that the proposed model improves the overall accuracy of the classification compared to traditional single view algorithms.",
keywords = "Multi-view learning, deep learning, image classification, neural network",
author = "Sara Bahaadini and Neda Rohani and Scott Coughlin and Michael Zevin and Vicky Kalogera and Katsaggelos, {Aggelos K.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952693",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2931--2935",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
address = "United States",
}