  The gravitational-wave (GW) detector data are affected by short-lived
instrumental or terrestrial transients, called glitches, which can simulate GW
signals. Mitigation of glitches is particularly difficult for algorithms which
target generic sources of short-duration GW transients (GWT), and do not rely
on GW waveform models to distinguish astrophysical signals from noise, such as
Coherent WaveBurst (cWB). This work is part of the long-term effort to mitigate
transient noises in cWB, which led to the introduction of specific estimators,
and a machine-learning based signal-noise classification algorithm. Here, we
propose an autoencoder neural network, integrated into cWB, that learns
transient noises morphologies from GW time-series. We test its performance on
the glitch family known as blip. The resulting sensitivity to generic GWT and
binary black hole mergers significantly improves when tested on LIGO detectors
data from the last observation period (O3b). At false alarm rate of one event
per 50 years the sensitivity volume increases up to 30% for signal morphologies
similar to blip glitches. In perspective, this tool can adapt to classify
different transient noise classes that may affect future observing runs,
enhancing GWT searches.
