Author(s): Bones Oliver C., Cox Trevor J., Davies William J.
Summary:
The present work involved a sound-sorting and category-labelling task that elicits rather than prescribes words used to describe sounds, allowing categorization strategies to emerge spontaneously and the interpretation of the principal dimensions of categorization using the generated descriptive words. Previous soundscape work suggests that ‘everyday listening’ is primarily concerned with gathering information about sound sources, and that sounds are typically categorized by perceived similarities between the sound-causing events. The present work demonstrates that this is likely to be the case when sound-sources are sufficiently differentiated for this to be a useful cognitive strategy, such as when categorizing a variety of different sound sources, or when categorizing a broad class of sounds with multiple sources such as ‘water’. However, distinct strategies based upon alternative cues emerge for other types of sounds. For example, categorization of dog sounds is primarily determined by judgements relating to perceptual dimensions similar to valence (‘sad’/’lonely’-‘playful’/’friendly’) and arousal (‘bored’/’whining’-‘threatening’/’vicious’), a finding that supports the circumplex model of affect as a meaningful framework for understanding human categorization of this type of sound. Categorization of engine sounds on the other hand was found to be based primarily upon explicit assessment of the acoustic signal, along dimensions which correlate strongly with the fluctuation strength (‘steady’-‘chugging’) and sharpness (‘muffled’-‘jarring’) of the recordings. These results demonstrate that categorization of sound is based upon different strategies depending on context and the availability of cues. It has implications for experimental methods in soundscapes that prescribe conceptual frameworks on test subjects. For instance, careful consideration should be given to the appropriateness of semantic differential scales in future perceptual soundscape work.
Name: Prof William Davies
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Country: United Kingdom