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.
See also
Examples
requireNamespace("psychotools") data(envirosound) set.seed(1019) ## Choice-model representation of unpleasantness mat <- summary(envirosound$unpleasantness, pcmatrix = TRUE) strans(mat)#> #> Stochastic Transitivity #> #> Violations ErrorRatio MeanDev MaxDev Deviance Df Pr(>Chi) #> Weak 0 0.00000 0.0000 0.0000 0 0 1 #> Moderate 1 0.00455 0.0405 0.0405 NA NA NA #> Strong 39 0.17727 0.0485 0.1622 NA NA NA #> --- #> Number of Tests: 220 #>btl1 <- eba(mat) eba1 <- eba(mat, A = list(c(1, 13), c(2, 13), c(3, 13), c(4, 13), c(5, 13), c(6, 13), c(7, 13), c(8, 13), c(9, 13), c(10, 13), c(11, 13), 12)) eba2 <- eba(mat, A = list(c(1, 13), c(2, 13), c(3, 13), c(4, 13), c(5, 13), c(6, 13), c(7, 13, 14), c(8, 13, 14), c(9, 13, 14), c(10, 13, 14), c(11, 13, 14), 12), s = runif(14)) anova(btl1, eba1, eba2)#> Analysis of Deviance Table #> #> Model 1: btl1 #> Model 2: eba1 #> Model 3: eba2 #> Resid. Df Resid. Dev Df Deviance Pr(>Chi) #> 1 55 84.188 #> 2 54 68.723 1 15.465 8.403e-05 *** #> 3 53 58.468 1 10.254 0.001364 ** #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1sounds <- psychotools::covariates(envirosound$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")## 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 #>
