README.md

In tuomaseerola/inconMore: More data for Computational Models of Simultaneous Consonance

inconMore - More data for Computational Models of Simultaneous Consonance

inconMore is an R library that supplements simultaneous consonance perception datasets originally provided by Peter Harrison’s wonderful incon library available at https://github.com/pmcharrison/incon. incon contains a range of roughness and harmonicity models and some perceptual datasets. Here we offer extra datasets.

Installation

You can install the current version of inconMore from Github by entering the following commands into R:

if (!require(devtools)) install.packages("devtools")
devtools::install_github("tuomaseerola/inconMore")

Datasets

Currently the following datasets are implemented:

| dataset | Stimulus N | Study | |:--------|:-----------|:------------------------------------| | art18 | 12 | Arthurs et al., 2018 | | pop19 | 80 | Popescu et al., 2019 | | lah20a | 25 | Lahdelma & Eerola 2020 (Exp. 1) | | lah20b | 72 | Lahdelma & Eerola 2020 (Exp. 2) | | bowl18 | 298 | Bowling et al., 2018 | | jl12a | 55 | Johnson-Laird et al., 2012 (Exp. 1) | | jl12b | 48 | Johnson-Laird et al., 2012 (Exp. 2) | | sch03 | 12 | Schwartz et al., 2003 | | lah16 | 15 | Lahdelma & Eerola, 2016 |

Usage

The primary function is inconMore, which contains several datasets.

library(inconMore)
data <- inconMore::art18 # Arthurs and Timmers 2018
knitr::kable(head(data))

| dataset | name | id | rating | pi_chord | chord_size | |:--------|:-------|:----|-------:|:---------------|------------:| | art18 | Major | c1 | 5.7 | 60, 64, 67 | 3 | | art18 | Minor | c2 | 4.8 | 60, 63, 67 | 3 | | art18 | Dim | c3 | 4.3 | 60, 63, 66 | 3 | | art18 | Aug | c4 | 3.7 | 60, 64, 68 | 3 | | art18 | Sus | c5 | 5.0 | 60, 65, 67 | 3 | | art18 | Major7 | c6 | 4.5 | 60, 64, 67, 71 | 4 |

See the package’s inbuilt documentation, ?inconMore, for further details.

Example

library(inconMore)
library(hrep)
library(incon)

chord <- hrep::pi_chord(inconMore::art18$pi_chord[[1]]) # major
incon(chord,model = 'hutch_78_roughness')
#> hutch_78_roughness 
#>          0.1202426
chord <- hrep::pi_chord(inconMore::art18$pi_chord[[3]]) # diminished
incon(chord,model = 'hutch_78_roughness')
#> hutch_78_roughness 
#>          0.2005575

Citation

Eerola, T. & Lahdelma, I. (2021). More data for Computational Models of Simultaneous Consonance.

References

Arthurs, Y., Beeston, A. V., & Timmers, R. (2018). Perception of isolated chords: Examining frequency of occurrence, instrumental timbre, acoustic descriptors and musical training. Psychology of Music, 46(5), 662–681.

Bowling, D. L., Purves, D., & Gill, K. Z. (2018). Vocal similarity predicts the relative attraction of musical chords. Proceedings of the National Academy of Sciences, 115(1), 216–221. https://doi.org/10.1073/pnas.1713206115

Johnson-Laird, P. N., Kang, O. E., & Leong, Y. C. (2012). On musical dissonance. Music Perception: An Interdisciplinary Journal, 30(1), 19-35. https://doi.org/10.1525/mp.2012.30.1.19

Lahdelma, I. & Eerola, T. (2016). Mild dissonance preferred over consonance in single chord perception. i-Perception, 7(3), 1-21. https://doi.org/10.1177/2041669516655812

Lahdelma, I. & Eerola, T. (2020). Cultural familiarity and musical expertise impact the pleasantness of consonance/dissonance but not its perceived tension. Scientific Reports(10), 8693. https://doi.org/10.1038/s41598-020-65615-8

Popescu, T., Neuser, M. P., Neuwirth, M., Bravo, F., Mende, W., Boneh, O., Moss, F. C., & Rohrmeier, M. (2019). The pleasantness of sensory dissonance is mediated by musical style and expertise. Scientific Reports, 9(1070). https://doi.org/10.1038/s41598-018-35873-8

Schwartz, D. A., Howe, C. Q., & Purves, D. (2003). The statistical structure of human speech sounds predicts musical universals. Journal of Neuroscience, 23(18), 7160–7168. https://doi.org/10.1523/JNEUROSCI.23-18-07160.2003

tuomaseerola/inconMore documentation built on Dec. 23, 2021, 1:02 p.m.