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Example-Based Framework for Perceptually Guided Audio Texture Generation
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AI
DOI：10.1109/TASLP.2024.3393741.png)
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摘要
En 中文
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.
Keyword:
Vectors
Semantics
Aerospace electronics
Training
Generative adversarial networks
Controllability
Feature extraction
Audio textures
controllability
analysis-by-synthesis
gaver sounds
stylegan
latent space exploration
期刊
I
IF:
5.1
论文数:
2.6K
被引数:
1.1W
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