The effects of rhythm and melody on auditory stream segregation

Szalárdy, Orsolya; Bendixen, Alexandra; Bőhm, Tamás M.; Davies, Lucy A.; Denham, Susan; Winkler, István

doi:10.1121/1.4865196

Cited by 21 publications

(20 citation statements)

References 46 publications

(90 reference statements)

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“…For example, within a stochastic background, the spectrotemporal regularity of a repeating cluster of synchronous tones causes them to stream together into a perceptual object distinct from the acoustic background (Elhilali, Xiang, Shamma, & Simon, 2009; Elhilali, Ma, Micheyl, Oxenham, & A, 2010). Indeed, temporal regularity also aids temporal/sequential segregation by allowing listeners to predict upcoming sounds (Dowling et al, 1987; Bendixen et al, 2010; Devergie et al, 2010; Szalárdy et al, 2014). …”

Section: Introductionmentioning

confidence: 99%

EEG signatures accompanying auditory figure-ground segregation

et al. 2016

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In everyday acoustic scenes, figure-ground segregation typically requires one to group together sound elements over both time and frequency. Electroencephalogram was recorded while listeners detected repeating tonal complexes composed of a random set of pure tones within stimuli consisting of randomly varying tonal elements. The repeating pattern was perceived as a figure over the randomly changing background. It was found that detection performance improved both as the number of pure tones making up each repeated complex (figure coherence) increased, and as the number of repeated complexes (duration) increased – i.e., detection was easier when either the spectral or temporal structure of the figure was enhanced. Figure detection was accompanied by the elicitation of the object related negativity (ORN) and the P400 event-related potentials (ERPs), which have been previously shown to be evoked by the presence of two concurrent sounds. Both ERP components had generators within and outside of auditory cortex. The amplitudes of the ORN and the P400 increased with both figure coherence and figure duration. However, only the P400 amplitude correlated with detection performance. These results suggest that 1) the ORN and P400 reflect processes involved in detecting the emergence of a new auditory object in the presence of other concurrent auditory objects; 2) the ORN corresponds to the likelihood of the presence of two or more concurrent sound objects, whereas the P400 reflects the perceptual recognition of the presence of multiple auditory objects and/or preparation for reporting the detection of a target object.

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Section: Introductionmentioning

confidence: 99%

EEG signatures accompanying auditory figure-ground segregation

et al. 2016

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“…Such a composite model would also better fit the variety of Gestalt principles governing the formation of perceptual objects. One issue that is common to almost all models reviewed here is that whereas previous research has shown that temporal regularity/predictability helps auditory stream segregation (e.g., Andreou et al, 2011; Rajendran et al, 2013; Szalárdy et al, 2014), present models do not explicitly handle the temporal structure of sound sequences. Thus future models should explore how more nuanced temporal features beyond simple presentation rate can be taken into account in modeling ASA.…”

Section: Summary and Future Directionsmentioning

confidence: 96%

“…Further, there is evidence that temporal coherence is indeed extracted in the human auditory system (O'Sullivan et al, 2015; Teki et al, 2016). However, in contrast to neural and Bayesian models, existing temporal-coherence models don't offer a clear path for capturing higher-order regularities within the sound input, which are known to help auditory stream segregation (Dowling, 1973; Bey and McAdams, 2002; Bendixen et al, 2010; Szalárdy et al, 2014). Further, by eliminating the formation of representations for alternative sound organizations, they do not account for multi-stable stimulus configurations.…”

Section: Summary and Future Directionsmentioning

confidence: 99%

“…However, it is not easy to see, how this system would encode longer temporal patterns, such as melodies. The presence of separate patterns/melodies is known to allow interleaved sound sequences to be segregated (e.g., Dowling, 1973; Bey and McAdams, 2002; Bendixen et al, 2010) even in the absence of additional cues (Szalárdy et al, 2014). …”

Section: Introductionmentioning

confidence: 99%

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Computational Models of Auditory Scene Analysis: A Review

2016

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Auditory scene analysis (ASA) refers to the process (es) of parsing the complex acoustic input into auditory perceptual objects representing either physical sources or temporal sound patterns, such as melodies, which contributed to the sound waves reaching the ears. A number of new computational models accounting for some of the perceptual phenomena of ASA have been published recently. Here we provide a theoretically motivated review of these computational models, aiming to relate their guiding principles to the central issues of the theoretical framework of ASA. Specifically, we ask how they achieve the grouping and separation of sound elements and whether they implement some form of competition between alternative interpretations of the sound input. We consider the extent to which they include predictive processes, as important current theories suggest that perception is inherently predictive, and also how they have been evaluated. We conclude that current computational models of ASA are fragmentary in the sense that rather than providing general competing interpretations of ASA, they focus on assessing the utility of specific processes (or algorithms) for finding the causes of the complex acoustic signal. This leaves open the possibility for integrating complementary aspects of the models into a more comprehensive theory of ASA.

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“…They found that randomizing either or both tone sequences decreased the proportion of time listeners reported the A and B sequences to be perceived as separate streams (also see Bendixen et al, 2013). Szalardy et al (2014) constructed a pair of interleaved tone sequences using the notes of two different familiar melodies for each sequence. They similarly found that the proportion of time listeners reported a percept of streaming was less when the tones and/or their timing were scrambled than when the tones faithfully followed the melodies.…”

Section: Introductionmentioning

confidence: 99%

Auditory streaming of tones of uncertain frequency, level, and duration

Chang

Lutfi

Lee

2015

The Journal of the Acoustical Society of America

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Stimulus uncertainty is known to critically affect auditory masking, but its influence on auditory streaming has been largely ignored. Standard ABA-ABA tone sequences were made increasingly uncertain by increasing the sigma of normal distributions from which the frequency, level, or duration of tones were randomly drawn. Consistent with predictions based on a model of masking by Lutfi, Gilbertson, Chang, and Stamas [J. Acoust. Soc. Am. 134, 2160–2170 (2013)], the frequency difference for which A and B tones formed separate streams increased as a linear function of sigma in tone frequency but was much less affected by sigma in tone level or duration.

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The effects of rhythm and melody on auditory stream segregation

Cited by 21 publications

References 46 publications

EEG signatures accompanying auditory figure-ground segregation

EEG signatures accompanying auditory figure-ground segregation

Computational Models of Auditory Scene Analysis: A Review

Auditory streaming of tones of uncertain frequency, level, and duration

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