• Corpus ID: 238856669

SpecSinGAN: Sound Effect Variation Synthesis Using Single-Image GANs

  title={SpecSinGAN: Sound Effect Variation Synthesis Using Single-Image GANs},
  author={Adri'an Barahona-R'ios and Tom Collins},
Single-image generative adversarial networks learn from the internal distribution of a single training example to generate variations of it, removing the need of a large dataset. In this paper we introduce SpecSinGAN, an unconditional generative architecture that takes a single one-shot sound effect (e.g., a footstep; a character jump) and produces novel variations of it, as if they were different takes from the same recording session. We explore the use of multi-channel spectrograms to train… 

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