Example-Based Framework for Perceptually Guided Audio Texture Generation

Generative models for synthesizing audio textures explicitly encode controllability by conditioning the model with labelled data. While datasets for audio textures can be easily recorded in-the-wild, semantically labeling them is expensive, time-consuming, and prone to errors due to human annotator subjectivity. Thus, to control generation, there is a need to automatically infer user-defined perceptual factors of variation in the latent space of a generative model while modelling unlabeled textures. In this paper, we propose an example-based framework to determine vectors to guide texture generation based on user-defined semantic attributes. By synthesizing a few synthetic examples to indicate the presence or absence of a semantic attribute, we can infer the guidance vectors in the latent space of a generative model to control that attribute during generation. Our results show that our method is capable of finding perceptually relevant and deterministic guidance vectors for controllable generation for both discrete as well as continuous textures. Furthermore, we demonstrate the application of this method to other tasks such as selective semantic attribute transfer.