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arXiv · 2308.11859

Example-Based Framework for Perceptually Guided Audio Texture Generation

Purnima Kamath·Chitralekha Gupta·Lonce Wyse·Suranga Nanayakkara
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Abstract

Controllable generation using 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.

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Purnima Kamath, Chitralekha Gupta, Lonce Wyse, Suranga Nanayakkara. 2024-04-14. Example-Based Framework for Perceptually Guided Audio Texture Generation. https://doi.org/10.1109/taslp.2024.3393741

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