Judging the similarity of soundscapes does not require categorization: evidence from spliced stimuli

This study uses an audio signal transformation, splicing, to create an experimental situation where human listeners judge the similarity of audio signals, which they cannot easily categorize. Splicing works by segmenting audio signals into 50-ms frames, then shuffling and concatenating these frames back in random order. Splicing a signal masks the identification of the categories that it normally elicits: For instance, human participants cannot easily identify the sound of cars in a spliced recording of a city street. This study compares human performance on both normal and spliced recordings of soundscapes and music. Splicing is found to degrade human similarity performance significantly less for soundscapes than for music: When two spliced soundscapes are judged similar to one another, the original recordings also tend to sound similar. This establishes that humans are capable of reconstructing consistent similarity relations between soundscapes without relying much on the identification of the natural categories associated with such signals, such as their constituent sound sources. This finding contradicts previous literature and points to new ways to conceptualize the different ways in which humans perceive soundscapes and music.