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Categorization of Broadcast Audio Objects in Complex Auditory Scenes
Author(s) -
James Woodcock,
William J. Davies,
Trevor J. Cox,
Frank Melchior
Publication year - 2016
Publication title -
journal of the audio engineering society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.234
H-Index - 60
ISSN - 1549-4950
DOI - 10.17743/jaes.2016.0007
Subject(s) - categorization , computer science , perception , rendering (computer graphics) , multidimensional scaling , object (grammar) , auditory scene analysis , speech recognition , cognition , variety (cybernetics) , artificial intelligence , psychology , machine learning , neuroscience
This paper presents a series of experiments to determine a categorization framework for broadcast audio objects. Object-based audio is becoming an evermore important paradigm for the representation of complex sound scenes. However, there is a lack of knowledge regarding object level perception and cognitive processing of complex broadcast audio scenes. As categorization is a fundamental strategy in reducing cognitive load, knowledge of the categories utilized by listeners in the perception of complex scenes will be beneficial to the development of perceptually based representations and rendering strategies for object-based audio. In this study, expert and non-expert listeners took part in a free card sorting task using audio objects from a variety of different types of programme material. Hierarchical agglomerative clustering suggests that there are 7 general categories, which relate to sounds indicating actions and movement, continuous and transient background sound, clear speech, non-diegetic music and effects, vocalisations, and prominent attention grabbing transient sounds. A three dimensional perceptual space calculated via multidimensional scaling suggests that these categories vary along dimensions related to the semantic content of the objects, the temporal extent of the objects, and whether the object indicates the presence of people.

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