BISAID: BIPOLAR SEMANTIC ADJECTIVES ICONS AND EARCONS DATASET

Abstract

This paper presents BiSAID, a dataset for exploring bipolar semantic adjectives in non-speech auditory cues, including earcons and auditory icons, i.e., sounds used to signify specific events or relay information in auditory interfaces from recorded or synthetic sources, respectively. In total, our dataset includes 599 non-speech auditory cues with different semantic labels, covering temperature (cold vs. warm), brightness (bright vs. dark), sharpness (sharp vs. dull), shape (curved vs. flat), and accuracy (correct vs. incorrect). Furthermore, we advance a preliminary analysis of brightness and accuracy earcon pairs from the BiSAID dataset to infer idiosyncratic sonic structures of each semantic earcon label from 66 instantaneous low- and mid-level descriptors, covering temporal, spectral, rhythmic, and tonal descriptors. Ultimately, we aim to unveil the relationship between sonic parameters behind earcon design, thus systematizing their structural foundations and shedding light on the metaphorical semantic nature of their description. This exploration revealed that spectral characteristics (e.g. spectral flux and spectral complexity) serve as the most relevant acoustic correlates in differentiating earcons on the dimensions of brightness and accuracy, respectively. The methodology holds great promise for systematizing earcon design and generating hypotheses for in-depth perceptual studies. (c) 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original.