2019
DOI: 10.1121/1.5091778
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Specifying the perceptual relevance of onset transients for musical instrument identification

Abstract: The following article appeared in K. Siedenburg (2019). Specifying the relevance of onset transients in musical instrument identification.

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Cited by 8 publications
(8 citation statements)
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“…Stimulus features, such as spectral power distributions (that translate into colour perception) or sound pressure level (that contributes to loudness perception) have been discovered and are well defined, yet it would be ignorant to assume that we already have a comprehensive understanding of all stimulus features that are relevant to human perception. For example, research has only recently established the perceptual relevance of quasi-stationary sinusoidal components in acoustic object identification [36].…”
Section: Perceptual Relevance: Recognition and Likingmentioning
confidence: 99%
“…Stimulus features, such as spectral power distributions (that translate into colour perception) or sound pressure level (that contributes to loudness perception) have been discovered and are well defined, yet it would be ignorant to assume that we already have a comprehensive understanding of all stimulus features that are relevant to human perception. For example, research has only recently established the perceptual relevance of quasi-stationary sinusoidal components in acoustic object identification [36].…”
Section: Perceptual Relevance: Recognition and Likingmentioning
confidence: 99%
“…The main experiment by Siedenburg (2019) serves as the starting point for the present modeling study. That experiment was divided into a training and a test phase.…”
Section: A Previous Experiments Used For Modelingmentioning
confidence: 99%
“…It has yet remained unclear whether this onset advantage is due to enhanced perceptual encoding or the richness of acoustical information during onsets. Here this issue was approached by modeling a recent study on instrument identification from tone excerpts [Siedenburg 2019, JASA, 145(2), 1078-1087. A simple Hidden Markov Model classifier with separable Gabor filterbank features simulated human performance and replicated the onset advantage observed previously for human listeners.…”
mentioning
confidence: 99%
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