  We describe our approach for the generative emotional vocal burst task (ExVo
Generate) of the ICML Expressive Vocalizations Competition. We train a
conditional StyleGAN2 architecture on mel-spectrograms of preprocessed versions
of the audio samples. The mel-spectrograms generated by the model are then
inverted back to the audio domain. As a result, our generated samples
substantially improve upon the baseline provided by the competition from a
qualitative and quantitative perspective for all emotions. More precisely, even
for our worst-performing emotion (awe), we obtain an FAD of 1.76 compared to
the baseline of 4.81 (as a reference, the FAD between the train/validation sets
for awe is 0.776).
