Noise of non-astrophysical origin will contaminate science data taken by the
Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and
Advanced Virgo gravitational-wave detectors. Prompt characterization of
instrumental and environmental noise transients will be critical for improving
the sensitivity of the advanced detectors in the upcoming science runs. During
the science runs of the initial gravitational-wave detectors, noise transients
were manually classified by visually examining the time-frequency scan of each
event. Here, we present three new algorithms designed for the automatic
classification of noise transients in advanced detectors. Two of these
algorithms are based on Principal Component Analysis. They are Principal
Component Analysis for Transients (PCAT), and an adaptation of LALInference
Burst (LIB). The third algorithm is a combination of an event generator called
Wavelet Detection Filter (WDF) and machine learning techniques for
classification. We test these algorithms on simulated data sets, and we show
their ability to automatically classify transients by frequency, SNR and
waveform morphology
