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In this paper we propose a conditioning trick, called difference departurefrom normality, applied on the generator network in response to instabilityissues during GAN training. We force the generator to get closer to thedeparture from normality function of real samples computed in the spectraldomain of Schur decomposition. This binding makes the generator amenable totruncation and does not limit exploring all the possible modes. We slightlymodify the BigGAN architecture incorporating residual network for synthesizing2D representations of audio signals which enables reconstructing high qualitysounds with some preserved phase information. Additionally, the proposedconditional training scenario makes a trade-off between fidelity and varietyfor the generated spectrograms. The experimental results on UrbanSound8k andESC-50 environmental sound datasets and the Mozilla common voice dataset haveshown that the proposed GAN configuration with the conditioning trickremarkably outperforms baseline architectures, according to three objectivemetrics: inception score, Frechet inception distance, and signal-to-noiseratio.

Conditioning Trick for Training Stable GANs