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Interest in neural audio synthesis has been growing lately both in academia and industry. Deep Learning (DL) synthesisers enable musicians to generate fresh, often completely unconventional sounds. However, most of these applications present a drawback. It is difficult for musicians to generate sounds which reflect the timbral properties they have in mind, because of the nature of the latent spaces of such systems. These spaces generally have large dimensionality and cannot easily be mapped todoi:10.5281/zenodo.7088415 fatcat:mns6riefsraotiveq2mudao764