pitch_app: Interactive pitch tracker
In soundgen: Sound Synthesis and Acoustic Analysis
pitch_app R Documentation
Interactive pitch tracker
Description
Starts a shiny app for manually editing pitch contours. The settings in the
panels on the left correspond to arguments to analyze - see
analyze and https://cogsci.se/soundgen/acoustic_analysis.html
for help and examples. You can verify the pitch contours first, and then feed
them back into analyze (see examples). Use hotkeys (eg spacebar to
play/stop) and avoid working with very large files.
Usage
pitch_app(...)
Arguments
...
presets like windowLength = 25, pitchMethods = c('autocor',
'cep'). Full list: dynamicRange, zp, nCands, minVoicedCands,
domThres, domSmooth, autocorThres, autocorSmooth, autocorUpsample,
autocorBestPeak, cepThres, cepZp, specThres, specPeak, specRatios,
specHNRslope, specSmooth, specMerge, specSinglePeakCert, hpsThres, hpsNum,
hpsNorm, hpsPenalty, zcThres, zcWin, certWeight, smooth, interpolCert,
spec_maxPoints, specContrast, specBrightness, blur_freq, blur_time,
reass_cex, osc_maxPoints, windowLength, step, silence, pitchFloor,
pitchCeiling, priorMean, priorSD, shortestSyl, shortestPause, interpolWin,
interpolTol, spec_cex, nColors, reass_windowLength, reass_step,
pitchMethods, summaryFun, summaryFun_text, spec_ylim,
spec_colorTheme, osc, wn
Value
A list with the last used settings ($settings) plus the output of
analyze() for each file from the last file queue with two additional
columns: "time" and "pitch". NB: only the results of the most recent file
queue are returned, so don't press "Load audio" repeatedly if you need the
output returned to R (the csv file with results should still be saved
correctly). When proceeding to the next file in the queue, the app saves to
disk a backup .csv file with one row per audio file. When the orange
"Download results" button is clicked, a context menu pops up offering to
terminate the app - if that happens, the results are also returned directly
into R. To process pitch contours further in R, work directly with
my_pitch[[myfile]]$detailed$time and
my_pitch[[myfile]]$detailed$pitch or, if loading the csv file, do
something like:
a = read.csv('~/Downloads/output.csv', stringsAsFactors = FALSE)
pitch = as.numeric(unlist(strsplit(a$pitch, ',')))
mean(pitch, na.rm = TRUE); sd(pitch, na.rm = TRUE)
Suggested workflow
Start by setting the basic analysis settings such as pitchFloor,
pitchCeiling, silence, etc. Then click "Load audio" to upload one or several
audio files (wav/mp3). Long files will be very slow, so please cut your audio
into manageable chunks (ideally <10 s). If Shiny complains that maximum
upload size is exceeded, you can increase it, say to 30 MB, with
options(shiny.maxRequestSize = 30 * 1024^2). Once the audio has been
uploaded to the browser, fine-tune the analysis settings as needed, edit the
pitch contour in the first file to your satisfaction, then click "Next" to
proceed to the next file, etc. Remember that setting a reasonable prior is
often faster than adjusting the contour one anchor at a time. When done,
click "Save results". If working with many files, you might want to save the
results occasionally in case the app crashes (although you should still be
able to recover your data if it does - see below).
How to edit pitch contours
Left-click to add a new anchor, double-click to remove it or unvoice the
frame. Each time you make a change, the entire pitch contour is re-fit, so
making a change in one frame can affect the path through candidates in
adjacent frames. You can control this behavior by changing the settings in
Out/Path and Out/Smoothing. If correctly configured, the app corrects the
contour with only a few manual values - you shouldn't need to manually edit
every single frame. For longer files, you can zoom in/out and navigate within
the file. You can also select a region to voice/unvoice or shift it as a
whole or to set a prior based on selected frequency range.
Recovering lost data
Every time you click "next" or "last" to move in between files in the queue,
the output you've got so far is saved in a temporary backup file. If the app
crashes or is closed without saving the results, this backup file preserves
your data. To recover it, restart pitch_app() - a dialog box will pop up and
ask whether you want to append the old data to the new one. Even so, save
your data regularly to be on the safe side!
See Also
formant_app annotation_app
Examples
## Not run:
# Recommended workflow for analyzing a lot of short audio files
path_to_audio = '~/Downloads/temp' # our audio lives here
# STEP 1: extract manually corrected pitch contours
my_pitch = pitch_app() # runs in default browser such as Firefox or Chrome
# To change system default browser, run something like:
options('browser' = '/usr/bin/firefox') # path to the executable on Linux
# You can pass presets with your preferred parameter values:
my_pitch = pitch_app(windowLength = 20, step = 10,
pitchMethods = c('dom', 'autocor', 'cep'), spec_ylim = c(0, 6))
# Object "my_pitch" contains the output, notably the time-pitch matrix
plot(my_pitch[[1]]$detailed$time, my_pitch[[1]]$detailed$pitch, type = 'b',
xlab = 'Time, ms', ylab = 'Pitch, Hz')
# Run the app with previously used settings
my_pitch2 = do.call(pitch_app, my_pitch$settings)
# save the complete output, including the settings used
saveRDS(my_pitch2, 'my_pitch_analysis.rds')
# STEP 2: run analyze() with manually corrected pitch contours to obtain
# accurate descriptives like the proportion of energy in harmonics above f0,
# etc. This also gives you formants and loudness estimates (disabled in
# pitch_app to speed things up)
df2 = analyze(
path_to_audio,
pitchMethods = 'autocor', # needed for calculating HNR
nFormants = 5, # now we can measure formants as well
pitchManual = my_pitch
# or, if loading the output of pitch_app() from the disk:
# pitchManual = '~/Downloads/output.csv'
# pitchManual = '~/path_to_some_folder/my_pitch_contours.rds
)
# STEP 3: add other acoustic descriptors, for ex.
df3 = segment(path_to_audio)
# STEP 4: merge df2, df3, df4, ... in R or a spreadsheet editor to have all
# acoustic descriptives together
# To verify your pitch contours and/or edit them later, copy output.csv to
# the folder with your audio, run pitch_app(), and load the audio + csv
# together. The saved pitch contours are treated as manual anchors
## End(Not run)
soundgen documentation built on Sept. 20, 2026, 5:07 p.m.
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| pitch_app | R Documentation |
Interactive pitch tracker
Description
Starts a shiny app for manually editing pitch contours. The settings in the
panels on the left correspond to arguments to analyze - see
analyze and https://cogsci.se/soundgen/acoustic_analysis.html
for help and examples. You can verify the pitch contours first, and then feed
them back into analyze (see examples). Use hotkeys (eg spacebar to
play/stop) and avoid working with very large files.
Usage
pitch_app(...)
Arguments
... |
presets like |
Value
A list with the last used settings ($settings) plus the output of
analyze() for each file from the last file queue with two additional
columns: "time" and "pitch". NB: only the results of the most recent file
queue are returned, so don't press "Load audio" repeatedly if you need the
output returned to R (the csv file with results should still be saved
correctly). When proceeding to the next file in the queue, the app saves to
disk a backup .csv file with one row per audio file. When the orange
"Download results" button is clicked, a context menu pops up offering to
terminate the app - if that happens, the results are also returned directly
into R. To process pitch contours further in R, work directly with
my_pitch[[myfile]]$detailed$time and
my_pitch[[myfile]]$detailed$pitch or, if loading the csv file, do
something like:
a = read.csv('~/Downloads/output.csv', stringsAsFactors = FALSE)
pitch = as.numeric(unlist(strsplit(a$pitch, ',')))
mean(pitch, na.rm = TRUE); sd(pitch, na.rm = TRUE)
Suggested workflow
Start by setting the basic analysis settings such as pitchFloor,
pitchCeiling, silence, etc. Then click "Load audio" to upload one or several
audio files (wav/mp3). Long files will be very slow, so please cut your audio
into manageable chunks (ideally <10 s). If Shiny complains that maximum
upload size is exceeded, you can increase it, say to 30 MB, with
options(shiny.maxRequestSize = 30 * 1024^2). Once the audio has been
uploaded to the browser, fine-tune the analysis settings as needed, edit the
pitch contour in the first file to your satisfaction, then click "Next" to
proceed to the next file, etc. Remember that setting a reasonable prior is
often faster than adjusting the contour one anchor at a time. When done,
click "Save results". If working with many files, you might want to save the
results occasionally in case the app crashes (although you should still be
able to recover your data if it does - see below).
How to edit pitch contours
Left-click to add a new anchor, double-click to remove it or unvoice the frame. Each time you make a change, the entire pitch contour is re-fit, so making a change in one frame can affect the path through candidates in adjacent frames. You can control this behavior by changing the settings in Out/Path and Out/Smoothing. If correctly configured, the app corrects the contour with only a few manual values - you shouldn't need to manually edit every single frame. For longer files, you can zoom in/out and navigate within the file. You can also select a region to voice/unvoice or shift it as a whole or to set a prior based on selected frequency range.
Recovering lost data
Every time you click "next" or "last" to move in between files in the queue, the output you've got so far is saved in a temporary backup file. If the app crashes or is closed without saving the results, this backup file preserves your data. To recover it, restart pitch_app() - a dialog box will pop up and ask whether you want to append the old data to the new one. Even so, save your data regularly to be on the safe side!
See Also
formant_app annotation_app
Examples
## Not run:
# Recommended workflow for analyzing a lot of short audio files
path_to_audio = '~/Downloads/temp' # our audio lives here
# STEP 1: extract manually corrected pitch contours
my_pitch = pitch_app() # runs in default browser such as Firefox or Chrome
# To change system default browser, run something like:
options('browser' = '/usr/bin/firefox') # path to the executable on Linux
# You can pass presets with your preferred parameter values:
my_pitch = pitch_app(windowLength = 20, step = 10,
pitchMethods = c('dom', 'autocor', 'cep'), spec_ylim = c(0, 6))
# Object "my_pitch" contains the output, notably the time-pitch matrix
plot(my_pitch[[1]]$detailed$time, my_pitch[[1]]$detailed$pitch, type = 'b',
xlab = 'Time, ms', ylab = 'Pitch, Hz')
# Run the app with previously used settings
my_pitch2 = do.call(pitch_app, my_pitch$settings)
# save the complete output, including the settings used
saveRDS(my_pitch2, 'my_pitch_analysis.rds')
# STEP 2: run analyze() with manually corrected pitch contours to obtain
# accurate descriptives like the proportion of energy in harmonics above f0,
# etc. This also gives you formants and loudness estimates (disabled in
# pitch_app to speed things up)
df2 = analyze(
path_to_audio,
pitchMethods = 'autocor', # needed for calculating HNR
nFormants = 5, # now we can measure formants as well
pitchManual = my_pitch
# or, if loading the output of pitch_app() from the disk:
# pitchManual = '~/Downloads/output.csv'
# pitchManual = '~/path_to_some_folder/my_pitch_contours.rds
)
# STEP 3: add other acoustic descriptors, for ex.
df3 = segment(path_to_audio)
# STEP 4: merge df2, df3, df4, ... in R or a spreadsheet editor to have all
# acoustic descriptives together
# To verify your pitch contours and/or edit them later, copy output.csv to
# the folder with your audio, run pitch_app(), and load the audio + csv
# together. The saved pitch contours are treated as manual anchors
## End(Not run)
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