  The detection of gravitational waves from core-collapse supernova (CCSN)
explosions is a challenging task, yet to be achieved, in which it is key the
connection between multiple messengers, including neutrinos and electromagnetic
signals. In this work, we present a method for detecting these kind of signals
based on machine learning techniques. We tested its robustness by injecting
signals in the real noise data taken by the Advanced LIGO-Virgo network during
the second observation run, O2. We trained a newly developed Mini-Inception
Resnet neural network using time-frequency images corresponding to injections
of simulated phenomenological signals, which mimic the waveforms obtained in 3D
numerical simulations of CCSNe. With this algorithm we were able to identify
signals from both our phenomenological template bank and from actual numerical
3D simulations of CCSNe. We computed the detection efficiency versus the source
distance, obtaining that, for signal to noise ratio higher than 15, the
detection efficiency is 70 % at a false alarm rate lower than 5%. We notice
also that, in the case of O2 run, it would have been possible to detect signals
emitted at 1 kpc of distance, whilst lowering down the efficiency to 60%, the
event distance reaches values up to 14 kpc.
