Deep Multi-view Models for Glitch Classification

Autor:	Bahaadini, Sara, Rohani, Neda, Coughlin, Scott, Zevin, Michael, Kalogera, Vicky, Katsaggelos, Aggelos K
Rok vydání:	2017
Předmět:	Computer Science - Learning Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'17)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1705.00034 Zobrazit plný text záznamu View this record from Arxiv