tidynorm (version 0.4.0)

norm_track_lobanov: Lobanov Track Normalization

Description

Lobanov Track Normalization

Usage

norm_track_lobanov(
  .data,
  ...,
  .token_id_col,
  .by = NULL,
  .time_col = NULL,
  .order = 5,
  .return_dct = FALSE,
  .drop_orig = FALSE,
  .names = "{.formant}_z",
  .silent = opt("tidynorm.silent")
)

Value

A data frame of Lobanov normalized formant tracks.

Arguments

.data

A data frame containing vowel formant data

...

<tidy-select> One or more unquoted expressions separated by commas. These should target the vowel formant data columns.

.token_id_col

<data-masking> A column that identifies token ids.

.by

<tidy-select> A selection of columns to group by. Typically a column of speaker IDs.

.time_col

<data-masking> A time column. (optional)

.order

The number of DCT parameters to use.

.return_dct

Whether or not the normalized DCT coefficients themselves should be returned.

.drop_orig

Should the originally targeted columns be dropped.

.names

A glue::glue() expression for naming the normalized data columns. The "{.formant}" portion corresponds to the name of the original formant columns.

.silent

Suppress normalization information messages when running a norm_*() function. (Defaults to FALSE, overwritable using option 'tidynorm.silent' or environment variable 'R_TIDYNORM_TIDYNORM_SILENT')

Details

$$ \hat{F}_{ij} = \frac{F_{ij} - L_i}{S_i} $$

$$ L_i = \frac{1}{N}\sum_{j=1}^{N}F_{ij} $$

$$ S_i = \sqrt{\frac{\sum(F_{ij}-L_i)^2}{N-1}} $$

Where

• \(\hat{F}\) is the normalized formant

• \(i\) is the formant number

• \(j\) is the token number

References

Lobanov, B. (1971). Classification of Russian vowels spoken by different listeners. Journal of the Acoustical Society of America, 49, 606–608.

Examples

library(tidynorm)
library(dplyr)
ggplot2_inst <- require(ggplot2)

track_subset <- speaker_tracks |>
  filter(
    .by = c(speaker, id),
    if_all(
      F1:F3,
      .fns = \(x) mean(is.finite(x)) > 0.9
    ),
    row_number() %% 2 == 1
  )

track_norm <- track_subset |>
  norm_track_lobanov(
    F1:F3,
    .by = speaker,
    .token_id_col = id,
    .time_col = t,
    .drop_orig = TRUE
  )

if (ggplot2_inst) {
  track_norm |>
    ggplot(
      aes(F2_z, F1_z, color = speaker)
    ) +
    stat_density_2d(bins = 4) +
    scale_x_reverse() +
    scale_y_reverse() +
    scale_color_brewer(palette = "Dark2") +
    coord_fixed()
}

# returning the DCT coefficients
track_norm_dct <- track_subset |>
  norm_track_lobanov(
    F1:F3,
    .by = speaker,
    .token_id_col = id,
    .time_col = t,
    .return_dct = TRUE,
    .drop_orig = TRUE,
    .names = "{.formant}_z"
  )

track_norm_means <- track_norm_dct |>
  summarise(
    .by = c(speaker, vowel, .param),
    across(
      ends_with("_z"),
      mean
    )
  ) |>
  reframe_with_idct(
    ends_with("_z"),
    .by = speaker,
    .token_id_col = vowel,
    .param_col = .param
  )


if (ggplot2_inst) {
  track_norm_means |>
    ggplot(
      aes(F2_z, F1_z, color = speaker)
    ) +
    geom_path(
      aes(
        group = interaction(speaker, vowel)
      )
    ) +
    scale_x_reverse() +
    scale_y_reverse() +
    scale_color_brewer(palette = "Dark2") +
    coord_fixed()
}