norm_barkz {tidynorm} | R Documentation |
Bark Difference Normalize
Description
Bark Difference Normalize
Usage
norm_barkz(
.data,
...,
.by = NULL,
.drop_orig = FALSE,
.keep_params = FALSE,
.names = "{.formant}_bz",
.silent = FALSE
)
Arguments
.data |
A data frame containing vowel formant data |
... |
|
.by |
|
.drop_orig |
Whether or not to drop the original formant data columns. |
.keep_params |
Whether or not to keep the Location ( |
.names |
A |
.silent |
Whether or not the informational message should be printed. |
Details
This is a within-token normalization technique. First all formants are converted to Bark (see hz_to_bark), then, within each token, F3 is subtracted from F1 and F2.
\hat{F}_{ij} = F_{ij} - L_j
L_j = F_{3j}
Value
A data frame of Bark Difference normalized formant values
References
Syrdal, A. K., & Gopal, H. S. (1986). A perceptual model of vowel recognition based on the auditory representation of American English vowels. The Journal of the Acoustical Society of America, 79(4), 1086–1100. doi:10.1121/1.393381
Examples
library(tidynorm)
ggplot2_inst <- require(ggplot2)
speaker_data_barkz <- speaker_data |>
norm_barkz(
F1:F3,
.by = speaker,
.names = "{.formant}_bz"
)
if (ggplot2_inst) {
ggplot(
speaker_data_barkz,
aes(
F2_bz,
F1_bz,
color = speaker
)
) +
stat_density_2d(
bins = 4
) +
scale_color_brewer(
palette = "Dark2"
) +
scale_x_reverse() +
scale_y_reverse() +
coord_fixed()
}