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Found 6 papers, 0 papers with code
Example-Based Framework for Perceptually Guided Audio Texture Generation
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.
Towards Controllable Audio Texture Morphing
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.
Parameter Sensitivity of Deep-Feature based Evaluation Metrics for Audio Textures
These metrics use deep features that summarize the statistics of any given audio texture, thus being inherently sensitive to variations in the statistical parameters that define an audio texture.
Sound Model Factory: An Integrated System Architecture for Generative Audio Modelling
We introduce a new system for data-driven audio sound model design built around two different neural network architectures, a Generative Adversarial Network(GAN) and a Recurrent Neural Network (RNN), that takes advantage of the unique characteristics of each to achieve the system objectives that neither is capable of addressing alone.
An Integrated System Architecture for Generative Audio Modeling
We introduce a new system for data-driven audio sound model design built around two different neural network architectures, a Generative Adversarial Network(GAN) and a Recurrent Neural Network (RNN), that takes advantage of the unique characteristics of each to achieve the system objectives that neither is capable of addressing alone.
Signal Representations for Synthesizing Audio Textures with Generative Adversarial Networks
Generative Adversarial Networks (GANs) currently achieve the state-of-the-art sound synthesis quality for pitched musical instruments using a 2-channel spectrogram representation consisting of log magnitude and instantaneous frequency (the "IFSpectrogram").
