Analysis of acoustic-semantic relationship for diversely annotated real-world audio data

A common problem of freely annotated or user contributed audio databases is the high variability of the labels, related to homonyms, synonyms, plurals, etc. Automatically re-labeling audio data based on audio similarity could offer a solution to this problem. This paper studies the relationship between audio and labels in a sound event database, by evaluating semantic similarity of labels of acoustically similar sound event instances. The assumption behind the study is that acoustically similar events are annotated with semantically similar labels. Indeed, for 43% of the tested data, there was at least one in ten acoustically nearest neighbors having a synonym as label, while the closest related term is on average one level higher or lower in the semantic hierarchy.