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GANSynth: Adversarial Neural Audio Synthesis

ICLR 2019 · Image generation › GANs · approach: GAN vocoders and audio synthesis · 277 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Represent audio with log-magnitude and instantaneous-frequency spectra rather than directly generated waveformsGANSynth · NSynthHuman Eval (wins): 485 vs 216Helped
Estimate instantaneous frequency rather than phase (ablation)Spectral GAN · NSynthHuman Eval (wins): 283 vs 203Helped
Increase STFT frequency resolution (ablation)Spectral GAN · NSynthNDB: 37.0 -> 29.3Helped
Increase STFT frequency resolution (ablation)IF-Mel GAN · NSynthHuman Eval (wins): 479 -> 485Helped
Apply mel frequency scaling (ablation)IF GAN · NSynthHuman Eval (wins): 283 -> 479Helped
Apply mel frequency scaling (ablation)IF + H GAN · NSynthHuman Eval (wins): 308 -> 485Helped
Use progressive training instead of non-progressive training (ablation)Waveform GAN · NSynth waveform generationFID: 447 -> 375Helped
Use progressive training instead of non-progressive training (ablation)Waveform GAN · NSynth waveform generationIS: 14.8 -> 2.5Hurt
Use 16-bit mixture-of-logistics output rather than 8-bit categorical output (ablation)WaveNet · NSynthFID: 320 -> 656Hurt
Generate with an IF-Mel GAN rather than WaveNetAudio generation · NSynth, four-second samplegeneration latency: 1077.53 seconds -> 20 millisecondsHelped
Use progressive rather than non-progressive spectral GAN training (ablation)Spectral GAN · NSynthsample qualityNo clear effect

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