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2014
DOI: 10.1121/1.4883387
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Room and head coloration can induce obligatory stream segregation

Abstract: Multiple sound reflections from room materials and a listener's head induce slight spectral modifications of sounds. This coloration depends on the listener and source positions, and on the room itself. This study investigated whether coloration could help segregate competing sources. Obligatory streaming was evaluated for diotic speech-shaped noises using a rhythmic discrimination task. Thresholds for detecting anisochrony were always significantly higher when stimuli differed in spectrum. The tested differen… Show more

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Cited by 8 publications
(10 citation statements)
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“…So far, this study has shown that listeners can perceive the regularities in interaural and spectral differences induced by a difference in simulated spatial location, despite large spectral variability from token to token (experiment 1), and that these regularities can be extracted to form auditory streams (experiment 2). This outcome is particularly interesting in the case of spectral differences associated with simulated spatial differences in the median plane, as it extends the results of Middlebrooks and Onsan (2012), David et al (2014), and David et al (2015) by showing that monaural spectral cues can induce streaming even in the presence of natural spectral variability between tokens. Martin et al (2012) showed only a slight spatial release from masking in the median plane using speech filtered by individualized head related transfer functions.…”
Section: E Discussionsupporting
confidence: 71%
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“…So far, this study has shown that listeners can perceive the regularities in interaural and spectral differences induced by a difference in simulated spatial location, despite large spectral variability from token to token (experiment 1), and that these regularities can be extracted to form auditory streams (experiment 2). This outcome is particularly interesting in the case of spectral differences associated with simulated spatial differences in the median plane, as it extends the results of Middlebrooks and Onsan (2012), David et al (2014), and David et al (2015) by showing that monaural spectral cues can induce streaming even in the presence of natural spectral variability between tokens. Martin et al (2012) showed only a slight spatial release from masking in the median plane using speech filtered by individualized head related transfer functions.…”
Section: E Discussionsupporting
confidence: 71%
“…However, those that have studied the effects of spectral spatial cues, independent of binaural cues, have found that alternating sequences of broadband noise bursts can be perceptually segregated based on small spectral differences between the stimuli (Middlebrooks and Onsan, 2012). Stream segregation based on these spectral cues can also be obligatory (David et al, 2014;David et al, 2015), in that segregation occurs even in situations where listeners are instructed to integrate the sequences into a single stream; for a discussion of voluntary and obligatory streaming, see Micheyl and Oxenham (2010).…”
Section: Introductionmentioning
confidence: 99%
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“…The power differences between the modulated and unmodulated bursts were within 1 dB in the region below 1 kHz, which suggested very limited perceivable differences. However, a recent study (David et al, 2014 ) has shown that very small spectral cues (a few dB difference in excitation pattern) could elicit obligatory streaming. It is then difficult to completely rule out that a 1 dB difference could not elicit voluntary streaming based on the current data set.…”
Section: Discussionmentioning
confidence: 98%
“…However, it is plausible that thresholds would be higher for more authentic musical stimuli (Butterfield, 2011), which are typically more varied and more complex. Indeed, numerous studies suggest that in general, the more varied the acoustic features (e.g., intensity, spectra) of the events within a pattern, the poorer temporal discrimination becomes (David et al, 2014;Divenyi and Danner, 1977;Grose et al, 2001;Penner, 1976;Phillips et al, 1997;Woods et al, 1979).…”
Section: Swing Detection Thresholdsmentioning
confidence: 99%