  Nearby core-collapse supernovae (CCSNe) are powerful multi-messenger sources
for gravitational-wave, neutrino and electromagnetic telescopes as they emit
gravitational waves in the ideal frequency band for ground based detectors.
Once a CCSN gravitational-wave signal is detected, we will need to determine
the parameters of the signal, and understand how those parameters relate to the
source's explosion, progenitor and remnant properties. This is a challenge due
to the stochastic nature of CCSN explosions, which is imprinted on their time
series gravitational waveforms. In this paper, we perform Bayesian parameter
estimation of CCSN signals using an asymmetric chirplet signal model to
represent the dominant high-frequency mode observed in spectrograms of CCSN
gravitational-wave signals. We use design sensitivity Advanced LIGO noise and
CCSN waveforms from four different hydrodynamical supernova simulations with a
range of different progenitor stars. We determine how well our model can
reconstruct time-frequency images of the emission modes, and show how well we
can determine parameters of the signal such as the frequency, amplitude, and
duration. We show how the parameters of our signal model may allow us to place
constraints on the proto-neutron star mass and radius, the turbulent kinetic
energy onto the proto-neutron star, and the time of shock revival.
