  The rise of deep learning algorithms has led many researchers to withdraw
from using classic signal processing methods for sound generation. Deep
learning models have achieved expressive voice synthesis, realistic sound
textures, and musical notes from virtual instruments. However, the most
suitable deep learning architecture is still under investigation. The choice of
architecture is tightly coupled to the audio representations. A sound's
original waveform can be too dense and rich for deep learning models to deal
with efficiently - and complexity increases training time and computational
cost. Also, it does not represent sound in the manner in which it is perceived.
Therefore, in many cases, the raw audio has been transformed into a compressed
and more meaningful form using upsampling, feature-extraction, or even by
adopting a higher level illustration of the waveform. Furthermore, conditional
on the form chosen, additional conditioning representations, different model
architectures, and numerous metrics for evaluating the reconstructed sound have
been investigated. This paper provides an overview of audio representations
applied to sound synthesis using deep learning. Additionally, it presents the
most significant methods for developing and evaluating a sound synthesis
architecture using deep learning models, always depending on the audio
representation.
