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Perception and Modeling of Affective Qualities of Musical Instrument Sounds across Pitch Registers

Overview of attention for article published in Frontiers in Psychology, February 2017
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Average Attention Score compared to outputs of the same age and source

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Title
Perception and Modeling of Affective Qualities of Musical Instrument Sounds across Pitch Registers
Published in
Frontiers in Psychology, February 2017
DOI 10.3389/fpsyg.2017.00153
Pubmed ID
Authors

Stephen McAdams, Chelsea Douglas, Naresh N. Vempala

Abstract

Composers often pick specific instruments to convey a given emotional tone in their music, partly due to their expressive possibilities, but also due to their timbres in specific registers and at given dynamic markings. Of interest to both music psychology and music informatics from a computational point of view is the relation between the acoustic properties that give rise to the timbre at a given pitch and the perceived emotional quality of the tone. Musician and nonmusician listeners were presented with 137 tones produced at a fixed dynamic marking (forte) playing tones at pitch class D# across each instrument's entire pitch range and with different playing techniques for standard orchestral instruments drawn from the brass, woodwind, string, and pitched percussion families. They rated each tone on six analogical-categorical scales in terms of emotional valence (positive/negative and pleasant/unpleasant), energy arousal (awake/tired), tension arousal (excited/calm), preference (like/dislike), and familiarity. Linear mixed models revealed interactive effects of musical training, instrument family, and pitch register, with non-linear relations between pitch register and several dependent variables. Twenty-three audio descriptors from the Timbre Toolbox were computed for each sound and analyzed in two ways: linear partial least squares regression (PLSR) and nonlinear artificial neural net modeling. These two analyses converged in terms of the importance of various spectral, temporal, and spectrotemporal audio descriptors in explaining the emotion ratings, but some differences also emerged. Different combinations of audio descriptors make major contributions to the three emotion dimensions, suggesting that they are carried by distinct acoustic properties. Valence is more positive with lower spectral slopes, a greater emergence of strong partials, and an amplitude envelope with a sharper attack and earlier decay. Higher tension arousal is carried by brighter sounds, more spectral variation and more gentle attacks. Greater energy arousal is associated with brighter sounds, with higher spectral centroids and slower decrease of the spectral slope, as well as with greater spectral emergence. The divergences between linear and nonlinear approaches are discussed.

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Uruguay 1 2%
Unknown 54 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 20%
Researcher 8 15%
Student > Bachelor 6 11%
Student > Doctoral Student 4 7%
Student > Ph. D. Student 4 7%
Other 11 20%
Unknown 11 20%
Readers by discipline Count As %
Psychology 14 25%
Arts and Humanities 9 16%
Neuroscience 5 9%
Computer Science 4 7%
Business, Management and Accounting 2 4%
Other 7 13%
Unknown 14 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 September 2017.
All research outputs
#13,674,509
of 24,226,848 outputs
Outputs from Frontiers in Psychology
#12,304
of 32,557 outputs
Outputs of similar age
#203,202
of 427,984 outputs
Outputs of similar age from Frontiers in Psychology
#266
of 463 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 32,557 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.8. This one has gotten more attention than average, scoring higher than 61% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 427,984 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 463 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.