Methods › Audio › Generative Audio Models › SpecGAN

SpecGAN

1 paper tagged archive 2025-07-28

Introduced by Chris Donahue et al. in Adversarial Audio Synthesis

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

SpecGAN is a generative adversarial network method for spectrogram-based, frequency-domain audio generation. The problem is suited for GANs designed for image generation. The model can be approximately inverted.

To process audio into suitable spectrograms, the authors perform the short-time Fourier transform with 16 ms windows and 8ms stride, resulting in 128 frequency bins, linearly spaced from 0 to 8 kHz. They take the magnitude of the resultant spectra and scale amplitude values logarithmically to better-align with human perception. They then normalize each frequency bin to have zero mean and unit variance. They clip the spectra to $3$ standard deviations and rescale to [−1, 1].

They then use the DCGAN approach on the result spectra.

PaperSource

Papers archive 2025-07-28

1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Audio Generation1
Audio Synthesis1
Image Generation1

Usage over time archive 2025-07-28

Papers per year tagged with SpecGAN: 2018 to 2018, peak 1 1 0 2018: 1 paper 2018
Papers per year the archive tags with this method, by the paper's archive date (1 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Generative Audio Models

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