This research investigates how the vocal mimicking capabilities of humans may be exploited to access and explore a given sonic space. Experiments showed that prototype vocal sounds can be represented in a two-dimensional space and still remain perceptually distinct from each other. Experiments provide a measure of how meaningful the machine distribution and grouping of vocal sounds are to humans, and confirms that humans are able to effectively use the acoustic and articulatory cues at their disposal to associate sounds to given prototypes. When used in an automatic clustering process, these cues are sufficiently consistent with those used by humans when categorizing acoustic phenomena. The procedure of dimensionality reduction and clustering is demonstrated in the case of imitations of engine sounds, which then represent the sonic space of a motor sound model. A two-dimensional space is particularly attractive for sound design because it can be used as a sonic map where the landmarks contain both a synthetic sound and its vocal imitation.
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