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OALib Journal期刊
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Probing auditory scene analysis

DOI: 10.3389/fnins.2014.00293

Keywords: auditory scene analysis, Multistable Perception, ambiguity, realistic auditory scenes, stream segregation

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Abstract:

In natural environments, the auditory system is typically confronted with a mixture of sounds originating from different sound sources. The sounds emanating from different sources can overlap each other in time and feature space. Thus, the auditory system has to continuously decompose competing sounds into distinct meaningful auditory objects or “auditory streams” associated with the possible sound sources. This decomposition of the sounds, termed “Auditory scene analysis” (ASA) by Albert Bregman (Bregman, 1990), involves two kinds of grouping. Grouping based on simultaneous cues (e.g., harmonicity) and on sequential cues (e.g., similarity of acoustic features over time). Understanding how the brain solves these tasks is a fundamental challenge facing auditory scientists. In recent years, the topic of ASA was broadly investigated in different fields of auditory research using a wide range of methods, including studies in different species (Fay, 2000; Fishman et al., 2001; Hulse et al., 1997; Moss & Surlykke, 2001), and computer modeling of ASA (for recent reviews see, Winkler et al., 2012; Gutschalk and Dykstra, 2014). Despite advances in understanding ASA, it still proves to be a major challenge for auditory research, especially in verifying whether experimental findings are transferable to more realistic auditory scenes. This special issue is a collection of ten research papers and one review paper providing a snapshot of current ASA research. The research paper on visual perception provides a comparative view of modality specific as well as general characteristics of perception. One approach for understanding ASA in real auditory scenes is the use of stimulus parameters that produce an ambiguous percept (cf., Pressnitzer et al., 2011). The advantage of such an approach is that different perceptual organizations can be studied without varying physical stimulus parameters. Using a visual ambiguous stimulus and combining real-time functional magnetic resonance imaging and machine learning techniques, Reichert et al. (2014) showed that it is possible to determine the momentary state of a subject’s conscious percept from time resolved BOLD-activity. The high classification accuracy of this data-driven classification approach may be particularly useful for auditory research investigating perception in continuous, ecologically-relevant sound scenes. A second advantage in using ambiguous stimuli in experiments on ASA is that perception of them can be influenced by intention or task (Moore and Gockel, 2002). By manipulating task requirements one can mirror

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