soundgen: Sound Synthesis and Acoustic Analysis

#' Shiny app defaults
#'
#' A list of default values for Shiny app soundgen_app() - mostly the same as
#' the defaults for soundgen(). NB: if defaults change, this has to be
#' updated!!!
"defaults"

#' Manual counts of syllables in 260 sounds
#'
#' A vector of the number of syllables in the corpus of 260 human non-linguistic
#' emotional vocalizations from Anikin & Persson (2017). The corpus can be
#' downloaded from http://cogsci.se/publications.html
"segmentManual"


#' Manual pitch estimation in 260 sounds
#'
#' A vector of manually verified pitch values per sound in the corpus of 590
#' human non-linguistic emotional vocalizations from Anikin & Persson (2017).
#' The corpus can be downloaded from http://cogsci.se/publications.html
"pitchManual"


#' Manually corrected pitch contours in 260 sounds
#'
#' A dataframe of 260 rows and two columns: "file" for filename in the corpus
#' (Anikin & Persson, 2017) and "pitch" for pitch values per frame. The corpus
#' can be downloaded from http://cogsci.se/publications.html
"pitchContour"


#' Conversion table from Hz to musical notation
#'
#' A dataframe of 192 rows and 2 columns: "note" and "freq" (Hz). Range: C-5
#' (0.51 Hz) to B10 (31608.53 Hz)
"notesDict"


#' Formants in American vowels
#'
#' Typical relative frequencies of the first four formants measured in dF units
#' (average spacing between formants, or formant dispersion) above or below
#' schwa based on estimated VTL in American English, from Hillenbrand (1995),
#' who measured F1-F4 in ~1.5K recordings (139 speakers, 12 vowels from each).
#' Audio and formant measurements are freely available online:
#' https://homepages.wmich.edu/~hillenbr/voweldata.html. The dataset below is
#' the result of modeling Hillenbrand's data with brms: mvbind(F1rel, F2rel) ~
#' vowel + (vowel|speaker). It shows the most credible location of each vowel
#' centroid in the F1Rel-F2Rel space.
#'
#' A dataframe of 12 observations and 5 columns: "vowel" = vowel (American
#' English), "F1Rel" to "F4Rel" = formant frequencies in dF relative to their
#' neutral, equidistant positions in a perfectly cylindrical vocal tract. See
#' \code{\link{schwa}} - this is what schwa() returns as $ff_relative_dF
#'
#' @references Hillenbrand, J., Getty, L. A., Clark, M. J., & Wheeler, K.
#'   (1995). Acoustic characteristics of American English vowels. The Journal of
#'   the Acoustical society of America, 97(5), 3099-3111.
#'
#' @examples
#' plot(hillenbrand$F1Rel, hillenbrand$F2Rel, type = 'n')
#' text(hillenbrand$F1Rel, hillenbrand$F2Rel, labels = hillenbrand$vowel)
"hillenbrand"

#' Nonlinear phenomena: Naive Bayes classifier trained on human nonverbal
#' vocalizations
#'
#' The results of running \code{\link{naiveBayes_train}} on acoustically
#' analyzed 969 human nonverbal vocalizations (>83K frames). It is used by
#' \code{\link{detectNLP}}.
"detectNLP_training_nonv"

#' Nonlinear phenomena: Naive Bayes classifier trained on synthetic sounds
#'
#' The results of running \code{\link{naiveBayes_train}} on 5000 synthetic
#' sounds with or without NLP created with soundgen(). It is used by
#' \code{\link{detectNLP}}.
"detectNLP_training_synth"