  Complex soft tissues, for example the knee meniscus, play a crucial role in
mobility and joint health, but when damaged are incredibly difficult to repair
and replace. This is due to their highly hierarchical and porous nature which
in turn leads to their unique mechanical properties. In order to design tissue
substitutes, the internal architecture of the native tissue needs to be
understood and replicated. Here we explore a combined audio-visual approach -
so called transperceptual - to generate artificial architectures mimicking the
native ones. The proposed method uses both traditional imagery, and sound
generated from each image as a method of rapidly comparing and contrasting the
porosity and pore size within the samples. We have trained and tested a
generative adversarial network (GAN) on the 2D image stacks. The impact of the
training set of images on the similarity of the artificial to the original
dataset was assessed by analyzing two samples. The first consisting of n=478
pairs of audio and image files for which the images were downsampled to 64
$\times$ 64 pixels, the second one consisting of n=7640 pairs of audio and
image files for which the full resolution 256 $\times$ 256 pixels is retained
but each image is divided into 16 squares to maintain the limit of 64 $\times$
64 pixels required by the GAN. We reconstruct the 2D stacks of artificially
generated datasets into 3D objects and run image analysis algorithms to
characterize statistically the architectural parameters - pore size, tortuosity
and pore connectivity - and compare them with the original dataset. Results
show that the artificially generated dataset that undergoes downsampling
performs better in terms of parameter matching. Our audiovisual approach has
the potential to be extended to larger data sets to explore both how
similarities and differences can be audibly recognized across multiple samples.
