soundgen (version 2.8.0)

pitch_app: 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 the vignette on acoustic analysis for help and examples. You can verify the pitch contours first, and then feed them back into analyze (see examples).

Usage

pitch_app(...)

Value

A list with the last used settings ($settings) plus the output of analyze() for each file with two additional columns: "time" and "pitch". When proceeding to the next file in the cue, two types of backups are created. (1) A global object called "my_pitch" is created or updated. This list becomes visible when the app is terminated, and it contains the usual outputs of analyze() ($detailed and $summary) plus lists of manually corrected voiced and voiceless frames. (2) The app saves to disk a .csv file with one row per audio file. Apart from the usual descriptives from analyze(), there are two additional columns: "time" with time stamps (the midpoint of each STFT frame, ms) and "pitch" with the manually corrected pitch values for each frame (Hz). When the orange "Download results" button is clicked, a context menu pops up offering to terminate the app - if that happens, the results are also returned directly into R. To process pitch contours further in R, work directly with my_pitch[[myfile]]$time and my_pitch[[myfile]]$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 backup file called "temp.csv", and the "my_pitch" global object is updated. If the app crashes or is closed without saving the results, this backup file preserves your data. To recover it, access this file manually on disk or simply restart pitch_app() - a dialog box will pop up and ask whether you wank to append the old data to the new one. Path to backup file: "[R_installation_folder]/soundgen/shiny/pitch_app/www/temp.csv", for example, "/home/allgoodguys/R/x86_64-pc-linux-gnu-library/3.6/soundgen/shiny/pitch_app/www/temp.csv"

Arguments

...

presets like windowLength = 25, pitchMethods = c('auocor', 'cep'). Full list: dynamicRange, zp, entropyThres, 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, pathfinding, spec_ylim, spec_colorTheme, osc, wn

See Also

formant_app

Examples

if (FALSE) {
# 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'), ylim = c(0.1, 3))
# full list of parameters that can be passed to pitch_app():
# paste0(c(names(input)[which(names(input) %in% rownames(defaults_analyze))],
#  'pitchMethods', 'summaryFun', 'summaryFun_text', 'pathfinding', 'spec_ylim',
#  'spec_colorTheme', 'osc', 'wn'), collapse = ', ')

# 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',  # only needed for 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
  # etc
)

# 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
}