  The way we perceive a sound depends on many aspects-- its ecological
frequency, acoustic features, typicality, and most notably, its identified
source. In this paper, we present the HCU400: a dataset of 402 sounds ranging
from easily identifiable everyday sounds to intentionally obscured artificial
ones. It aims to lower the barrier for the study of aural phenomenology as the
largest available audio dataset to include an analysis of causal attribution.
Each sample has been annotated with crowd-sourced descriptions, as well as
familiarity, imageability, arousal, and valence ratings. We extend existing
calculations of causal uncertainty, automating and generalizing them with word
embeddings. Upon analysis we find that individuals will provide less polarized
emotion ratings as a sound's source becomes increasingly ambiguous; individual
ratings of familiarity and imageability, on the other hand, diverge as
uncertainty increases despite a clear negative trend on average.

Audio and Speech Processing;Computation and Language;Sound