  In this paper, we propose a data-driven approach to train a Generative
Adversarial Network (GAN) conditioned on "soft-labels" distilled from the
penultimate layer of an audio classifier trained on a target set of audio
texture classes. We demonstrate that interpolation between such conditions or
control vectors provides smooth morphing between the generated audio textures,
and shows similar or better audio texture morphing capability compared to the
state-of-the-art methods. The proposed approach results in a well-organized
latent space that generates novel audio outputs while remaining consistent with
the semantics of the conditioning parameters. This is a step towards a general
data-driven approach to designing generative audio models with customized
controls capable of traversing out-of-distribution regions for novel sound
synthesis.
