Towards Controllable Audio Texture Morphing

• DOI: 10.48550/arxiv.2304.11648
• Publication Date: 2023-06-04
• Authors: Gupta, Chitralekha; Kamath, Purnima; Wei, Yize; Li, Zhuoyao; Nanayakkara, Suranga; Wyse, Lonce
• Keywords: FOS: Computer and information sciences; Sound (cs.SD); Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Audio and Speech Processing (eess.AS); FOS: Electrical engineering, electronic engineering, information engineering; Computer Science - Sound; Electrical Engineering and Systems Science - Audio and Speech Processing

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.<br/>accepted to ICASSP 2023<br/>