  We present a rapid stellar core-collapse waveform emulator built using a deep
convolutional generative adversarial network (DCGAN). The DCGAN was trained on
the Richers \textit{et al.~}\cite{richers:2017} waveform catalogue to learn the
structure of rotating stellar core-collapse gravitational-wave signals and
generate realistic waveforms. We show that the DCGAN learns the distribution of
the training data reasonably well, and that the waveform emulator produces
signals that appear to have the key features of core-collapse, bounce, early
post-bounce, and ringdown oscillations of the early proto-neutron star. The
pre-trained DCGAN can therefore be used as a phenomenological model for
rotating stellar core-collapse gravitational-waves.
