Synthesising knocking sound effects using conditional WaveGAN
Adrián Barahona-Ríos, Sandra Pauletto · Zenodo (CERN European Organization for Nuclear Research) · 2020
In this paper we explore the synthesis of sound effects using conditional generative adversarial networks (cGANs). We commissioned Foley artist Ulf Olausson to record a dataset of knocking sound effects with different emotions and trained a cGAN on it. We analysed the resulting synthesised sound effects by comparing their temporal acoustic features to the original dataset and by performing an online listening test. Results show that the acoustic features of the synthesised sounds are similar to those of the recorded dataset. Additionally, the listening test results show that the synthesised sounds can be identified by people with experience in sound design, but the model is not far from fooling non-experts. Moreover, on average most emotions can be recognised correctly in both recorded and synthesised sounds. Given that the temporal acoustic features of the two datasets are highly similar, we hypothesise that they strongly contribute to the perception of the intended emotions in the recorded and synthesised knocking sounds.