  The following article introduces a new parametric synthesis algorithm for
sound textures inspired by existing methods used for visual textures. Using a
2D Convolutional Neural Network (CNN), a sound signal is modified until the
temporal cross-correlations of the feature maps of its log-spectrogram resemble
those of a target texture. We show that the resulting synthesized sound signal
is both different from the original and of high quality, while being able to
reproduce singular events appearing in the original. This process is performed
in the time domain, discarding the harmful phase recovery step which usually
concludes synthesis performed in the time-frequency domain. It is also
straightforward and flexible, as it does not require any fine tuning between
several losses when synthesizing diverse sound textures. A way of extending the
synthesis in order to produce a sound of any length is also presented, after
which synthesized spectrograms and sound signals are showcased. We also discuss
on the choice of CNN, on border effects in our synthesized signals and on
possible ways of modifying the algorithm in order to improve its current long
computation time.
