Auditory Unpleasantness of Environmental Sound
envirosound.RdZimmer et al. (2004) investigated the auditory unpleasantness of twelve short binaural recordings (Johannsen and Prante, 2001); recordings were presented via headphones to 74 participants.
data(envirosound)Format
A data frame containing 74 observations on 2 variables:
- unpleasantness
paired comparison of class
paircomp; judgments for all 66 paired comparisons from 12 recordings: circular saw, stadium, dentist's drill, waterfall, ship's horn, stone in well, typewriter, hooves, fan, howling wind, tyre on gravel, wasp.- rt
median response time.
Details
Details of the recordings, including psychoacoustic metrics, are available
as an attribute of the unpleasantness variable (see Examples).
Source
Zimmer, K., Ellermeier, W., & Schmid, C. (2004). Using probabilistic choice models to investigate auditory unpleasantness. Acta Acustica united with Acustica, 90(6), 1019–1028.
References
Johannsen, K., & Prante, H.U. (2001). Environmental sounds for psychoacoustic testing. Acta Acustica united with Acustica, 87(2), 290–293.
Examples
requireNamespace("psychotools")
data(envirosound)
set.seed(1019)
## Choice-model representation of unpleasantness
mat <- summary(envirosound$unpleasantness, pcmatrix = TRUE)
strans(mat)
#>
#> Stochastic Transitivity
#>
#> Violations ErrorRatio MeanDev MaxDev Deviance Df Pr(>Chi)
#> Weak 0 0.00000 0.0000 0.0000 0 0 1
#> Moderate 1 0.00455 0.0405 0.0405 NA NA NA
#> Strong 39 0.17727 0.0485 0.1622 NA NA NA
#> ---
#> Number of Tests: 220
#>
btl1 <- eba(mat)
eba1 <- eba(mat, A = list(c(1, 13), c(2, 13), c(3, 13), c(4, 13),
c(5, 13), c(6, 13), c(7, 13), c(8, 13),
c(9, 13), c(10, 13), c(11, 13), 12))
eba2 <- eba(mat, A = list(c(1, 13), c(2, 13), c(3, 13), c(4, 13),
c(5, 13), c(6, 13), c(7, 13, 14), c(8, 13, 14),
c(9, 13, 14), c(10, 13, 14), c(11, 13, 14), 12),
s = runif(14))
anova(btl1, eba1, eba2)
#> Analysis of Deviance Table
#>
#> Model 1: btl1
#> Model 2: eba1
#> Model 3: eba2
#> Resid. Df Resid. Dev Df Deviance Pr(>Chi)
#> 1 55 84.188
#> 2 54 68.723 1 15.465 8.403e-05 ***
#> 3 53 58.468 1 10.254 0.001364 **
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
sounds <- psychotools::covariates(envirosound$unpleasantness)
sounds$u <- 10 * uscale(eba2, norm = 9) # u(fan) := 10
plot(magnitude ~ u, sounds, log = "x", type = "n",
xlab = "Indirect scaling (EBA model)",
ylab = "Direct magnitude estimation",
main = "Auditory unpleasantness of environmental sound")
mtext("(Zimmer et al., 2004)", line = 0.5)
abline(lm(magnitude ~ log10(u), sounds))
text(magnitude ~ u, sounds, labels = abbreviate(rownames(sounds), 4))
## Predicting unpleasantness from psychoacoustic metrics
summary(
lm(log(u) ~ scale(sharpness, scale = FALSE) +
scale(roughness, scale = FALSE):I(loudness.5 > 27),
sounds[-12, ]) # w/o wasp
)
#>
#> Call:
#> lm(formula = log(u) ~ scale(sharpness, scale = FALSE) + scale(roughness,
#> scale = FALSE):I(loudness.5 > 27), data = sounds[-12, ])
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.46560 -0.20242 0.03711 0.19697 0.34740
#>
#> Coefficients:
#> Estimate Std. Error
#> (Intercept) 2.52046 0.09993
#> scale(sharpness, scale = FALSE) 0.68183 0.07198
#> scale(roughness, scale = FALSE):I(loudness.5 > 27)FALSE -0.32407 0.10772
#> scale(roughness, scale = FALSE):I(loudness.5 > 27)TRUE 0.33319 0.12661
#> t value Pr(>|t|)
#> (Intercept) 25.222 3.93e-08 ***
#> scale(sharpness, scale = FALSE) 9.472 3.06e-05 ***
#> scale(roughness, scale = FALSE):I(loudness.5 > 27)FALSE -3.008 0.0197 *
#> scale(roughness, scale = FALSE):I(loudness.5 > 27)TRUE 2.632 0.0338 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.3249 on 7 degrees of freedom
#> Multiple R-squared: 0.9312, Adjusted R-squared: 0.9017
#> F-statistic: 31.59 on 3 and 7 DF, p-value: 0.0001933
#>