Calculating zero-crossing-rate in [s,ʃ]
Jonathan Harrington
WiSe 2122
1 Preliminaries
1.1 General setup
Follow the setup instructions given here, i.e. download R and RStudio, create a directory on your computer where you will store files on this course, make a note of the directory path, create an R project that accesses this directory, and install all indicated packages.
For this and subsequent tutorials, access the tidyverse,magrittr, emuR, and wrassp libraries:
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.4 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(magrittr)##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
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## set_names## The following object is masked from 'package:tidyr':
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## extractlibrary(emuR)##
## Attaching package: 'emuR'## The following object is masked from 'package:base':
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## normlibrary(wrassp)
sourceDir = "./testsample"
targetDir = "./emu_databases"1.2 Download and unzip
a = "https://www.phonetik.uni-muenchen.de/~jmh/"
b = "lehre/Rdf/kiel_butter1.zip"
down_path = paste(a, b, sep="/")
download.file(down_path,
file.path(sourceDir, "kiel_butter1.zip"))
# Create directory in sourceDir called 'butter'
dir.create(file.path(sourceDir, "butter"))
# unzip the file into that directory
unzip(file.path(sourceDir, "kiel_butter1.zip"),
exdir = file.path(sourceDir, "butter"))1.3 Convert the collection and create Emu database
path.butter = file.path(sourceDir, "butter")
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## [97] "K80BUTT3.wav"convert_TextGridCollection(path.butter,
dbName = "butter",
targetDir = targetDir)## INFO: Loading TextGridCollection containing 48 file pairs...
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|======================================================================| 100%Create the Emu database
butter_DB =
load_emuDB(file.path(targetDir, "butter_emuDB"))## INFO: Loading EMU database from ./emu_databases/butter_emuDB... (48 bundles found)
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|======================================================================| 100%1.4 Analysis
Identify [s, S] segments
fric.s = query(butter_DB, "Phonetic = s|S")Calculate ZCR on the fly
fric.zcr =
get_trackdata(butter_DB, fric.s,
onTheFlyFunctionName = "zcrana")##
## INFO: applying zcrana to 999 segments/events
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|======================================================================| 100%First plot
fric.zcr %>%
ggplot +
aes(y = T1, x = times_norm, col=labels, group=sl_rowIdx) +
geom_line()
Normalise length
fric.zcr.norm = normalize_length(fric.zcr, N = 11)Prepare data for an aggregate plot
fric.zcr.norm.m =
fric.zcr.norm %>%
group_by(times_norm, labels) %>%
summarise(T1 = mean(T1)) %>% ungroup## `summarise()` has grouped output by 'times_norm'. You can override using the `.groups` argument.Aggregate normalised time plot
fric.zcr.norm.m %>%
ggplot +
aes(y = T1, x = times_norm, col=labels, group=labels) +
geom_line() +
ylab("Zero-crossing-rate (Hz)")+
xlab("Proportional time")