Example-Based Framework for Perceptually Guided Audio Texture Generation

2024-01-01

Purnima Kamath *

Chitralekha Gupta

Lonce Wyse

Suranga Nanayakkara

DOI：10.1109/TASLP.2024.3393741

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Controllable generation in StyleGANs is usually achieved by training the model using labeled data. For audio textures, however, there is currently a lack of large semantically labeled datasets. Therefore, to control generation, we develop a method for semantic control over an unconditionally trained StyleGAN in the absence of such labeled datasets. In this paper, we propose an example-based framework to determine guidance vectors for audio texture generation based on user-defined semantic attributes. Our approach leverages the semantically disentangled latent space of an unconditionally trained StyleGAN. By using a few synthetic examples to indicate the presence or absence of a semantic attribute, we infer the guidance vectors in the latent space of the StyleGAN to control that attribute during generation. Our results show that our framework can find user-defined and perceptually relevant guidance vectors for controllable generation for audio textures. Furthermore, we demonstrate an application of our framework to other tasks, such as selective semantic attribute transfer.

Keywords:

Vectors

Semantics

Aerospace electronics

Training

Generative adversarial networks

Controllability

Feature extraction

Audio textures

controllability

analysis-by-synthesis

gaver sounds

stylegan

latent space exploration

Example-Based Framework for Perceptually Guided Audio Texture Generation

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