  This article introduces a new parametric synthesis method for sound textures
based on existing works in visual and sound texture synthesis. Starting from a
base sound signal, an optimization process is performed until the
cross-correlations between the feature-maps of several untrained 2D
Convolutional Neural Networks (CNN) resemble those of an original sound
texture. We use compressed RI spectrograms as input to the CNN: this
time-frequency representation is the stacking of the real and imaginary part of
the Short Time Fourier Transform (STFT) and thus implicitly contains both the
magnitude and phase information, allowing for convincing syntheses of various
audio events. The optimization is however performed directly on the time signal
to avoid any STFT consistency issue. The results of an online perceptual
evaluation are also detailed, and show that this method achieves results that
are more realistic-sounding than existing parametric methods on a wide array of
textures.
