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generate_mts_measures.r
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generate_mts_measures.r
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# Author: Patrick Reidy
# Purpose: Demo multitaper spectral analysis for Jane Stuart-Smith.
# Date: 2018-05-23
# Modified by: Michael Goodale
# Purpose: ISCAN Token import/export tutorial
# Date: 2019-05-23
# modifed by: Jane Stuart-Smith
# Purpose: first of all to create stripped script to produce only a few measures for piloting purposes. Have removed a lot of the explanatory stuff from Pat Reidy for me. Have recorded tips and wrinkles, futher work to do, plus initial comments for protocol here.
# Date: 2020-04-09
# modified by: James Tanner
# Purpose: process input data as command-line arguments
# Date: 2020-09-29
# modified by: James Tanner
# Purpose: convert to functions
# Data: 2021-03-28
started_at <- date()
library(ggplot2)
library(magrittr)
library(multitaper)
library(tibble)
library(tuneR)
library(doParallel)
library(foreach)
library(argparse)
library(stringr)
library(svMisc)
## Process comamand-line arguments
parser <- ArgumentParser(description = "Generate multitaper sibilant measurements")
parser$add_argument("input_file", help = "CSV file containing sibilants observations to measure")
parser$add_argument("sound_dir", help = "Path to the top-level directory containg the audio files")
parser$add_argument("output_dir", help = "Directory to write the mts-measured CSV file")
parser$add_argument("--directories", "-d", help = "The audio file contains speaker-level subdirectories", action = "store_true", default = FALSE)
parser$add_argument("--numbers", "-n", help = "Speaker names are defined with numbers (integers) instead of letters", action = "store_true", default = FALSE)
parser$add_argument("--speakers", "-s", help = "Use speaker codes for audio file names", action = "store_true", default = FALSE)
args <- parser$parse_args()
# The R files in the ./auxiliary subdirectory of this demo define a handful of
# S4 classes, generics, and methods that wrap functionality from the tuneR and
# multitaper packages.
# You'll need to source the R files in this order because, e.g., definitions
# in later files depend on S4 classes defined in earlier files.
source('./auxiliary/Waveform.R') # For reading .wav files.
source('./auxiliary/Spectrum.R') # Base methods shared by all spectrum-like objects.
source('./auxiliary/Periodogram.R') # For estimating spectra using the periodogram.
source('./auxiliary/DPSS.R') # Windowing functions for multitaper spectra.
source('./auxiliary/Multitaper.R') # For estimating spectra using multitaper method.
# JM: to rebuild most of the Multitaper S4 object for using Pat's measurement functions at a later time:
rebuildMultitaper <- function(token_id, corpus_data, all_multitapers){
new(Class = 'Multitaper',
values = as.numeric(all_multitapers$values[corpus_data$phone_id==token_id,]),
binWidth = all_multitapers$binWidth,
nyquist = all_multitapers$nyquist,
k = all_multitapers$k,
nw = all_multitapers$nw)
# tapers = .dpss)
}
rebuildMultitaperByRow <- function(row, all_multitapers){
new(Class = 'Multitaper',
values = as.numeric(all_multitapers$values[row,]),
binWidth = all_multitapers$binWidth,
nyquist = all_multitapers$nyquist,
k = all_multitapers$k,
nw = all_multitapers$nw)
# tapers = .dpss)
}
get_file_path <- function(corpus_data, row, sound_file_directory, subdirs){
if (args$speakers){
sound_file <- paste0(corpus_data[row, "speaker"], '.wav')
}else if ('sound_file_name'%in%names(corpus_data)){
sound_file <- paste0(gsub(".WAV", "", corpus_data[row, "sound_file_name"]), '.wav')
}else if ('recording'%in%names(corpus_data)){
sound_file <- paste0(corpus_data[row, "recording"], '.wav')
}else{
sound_file <- paste0(corpus_data[row, "discourse"], '.wav')
}
sound_file <- gsub('.wav.wav','.wav',sound_file) # because some of them already have .wav at the end
if (subdirs){
file.path(sound_file_directory, corpus_data[row, "speaker"], sound_file)
}else{
file.path(sound_file_directory, sound_file)
}
}
read_dataset <- function(filepath, sound_dir) {
## extract corpus name from dataset filename
cat("File: ", filepath, "\n")
corpus_name <- str_match(filepath, "([A-Za-z0-9_-]*)\\_sibilants\\.csv")[,2]
cat("Corpus name:", "\t", corpus_name, "\n")
# get the directory of the sound file and
# read in the CSV
sound_file_directory <- sound_dir
corpus_data <- read.csv(filepath)
if (args$numbers){
corpus_data$discourse <- sprintf("%03d",corpus_data$discourse)
}
return(list(corpus_data, corpus_name, sound_file_directory))
}
get_phone_time <- function(data, time, row) {
t <- data[row, time]
return(t)
}
# https://stackoverflow.com/questions/7824912/max-and-min-functions-that-are-similar-to-colmeans
colMax <- function (colData) {
apply(colData, MARGIN=c(2), max)
}
colSD <- function (colData) {
apply(colData, MARGIN=c(2), sd)
}
parallelized <- TRUE
n_cores <- 20
measuring <- TRUE
if (measuring){
## Get the corpus name from the input file
dataset = read_dataset(args$input_file, args$sound_dir)
corpus_data = dataset[[1]]
corpus_name = dataset[[2]]
sound_file_directory = dataset[[3]]
if(parallelized) {
registerDoParallel(n_cores)
}else{
n_cores = 1
}
#JM: open the first token to gather information
file_path <- get_file_path(corpus_data, row=1, sound_file_directory, args$directories)
begin <- get_phone_time(corpus_data, "phone_begin", 1)
end <- get_phone_time(corpus_data, "phone_end", 1)
file_midpoint <- begin + (end-begin) / 2
sock.x <- readWave(filename = file_path, from = file_midpoint - 0.0125, to = file_midpoint + 0.0125, units='seconds')
sock.x <- downsample(sock.x, 22050)
sock <- Waveform(sock.x)
sock_spectrum <- sock %>% Multitaper(k = 8, nw = 4)
#JM: save all the multitaper values
all_multitapers <- list(values = NULL,
frequencies = seq(from = 0, to = sock_spectrum@nyquist, by = sock_spectrum@binWidth),
binWidth = sock_spectrum@binWidth,
nyquist = sock_spectrum@nyquist,
k = sock_spectrum@k,
nw = sock_spectrum@nw)
n_values <- length(sock_spectrum@values)
mts_colnames <- paste0('S',1:n_values)
corpus_data[,mts_colnames] <- NA
#Split 0:nrows into (roughly) equal sized batches that are in order
batch_indices <- rep(0:(n_cores-1), each=(nrow(corpus_data) %/% n_cores))
batch_indices <- c(rep(0, nrow(corpus_data)-length(batch_indices)), batch_indices)
batches <- split(1:nrow(corpus_data), batch_indices)
# corpus_data <- subset(corpus_data, !discourse %in% c('ntn0290b','ntn0200a','ntn0370b','ntn0240a'))
corpus_data <- foreach(batch=batches, .combine=rbind) %dopar% {
corpus_data <- corpus_data[batch, ]
# print(corpus_data)
for (row in 1:nrow(corpus_data)){
# for (row in 1:200){
cat(round(row/nrow(corpus_data),6), "\r")
#To use for non-speaker directory corpora, just remove the speaker name and "/" from the paste function here.
#sound_file <- paste(corpus_data[row, "speaker_name"], "/", corpus_data[row, "sound_file_name"], '.wav', sep="")
# sound_file <- paste(corpus_data[row, "sound_file_name"], '.wav', sep="")
#JM: to handle column names of csv files made by sibilant.py:
file_path <- get_file_path(corpus_data, row, sound_file_directory, args$directories)
begin <- corpus_data[row, "phone_begin"]
end <- corpus_data[row, "phone_end"]
file_midpoint <- begin + (end-begin) / 2
# print(file_path)
# print(begin)
# print(end)
# print(file_midpoint)
# Read the contents of the wav file.
if(!file.exists(file_path)){
next
}
# print('a')
# print(file_path)
# print (corpus_data[1,1:23])
#JM: this seems like a more straightforward way to open and downsample, but I may be misunderstanding why it was originally done differently
# print (paste0("sock.x <- readWave(filename = ",file_path,", from = ",file_midpoint," - 0.0125, to = ",file_midpoint," + 0.0125, units='seconds')"))
sock.x <- readWave(filename = file_path, from = file_midpoint - 0.0125, to = file_midpoint + 0.0125, units='seconds')
# print('a1')
sock.x <- downsample(sock.x, 22050)
# print('a2')
sock <- Waveform(sock.x)
# print('b')
#want to run first of all without downsampling. IcE-Can apparently is 44100Hz sampling rate
# sock <- Waveform(waveform = file_path, from = file_midpoint - 0.020, to = file_midpoint + 0.020)
## this hopefully will downsample for me... NOTE this will also take the 25ms window which is as per K et al.
#sockb = downsample(socka[length(socka@left)/2+seq([email protected]*0.0125,[email protected]*0.0125)], 22050)
#sockc = Waveform(waveform = sockb)
#sock = sockc
#Estimate the spectrum of sock using the multitaper method. no preemphasis or zeropadding.
sock_spectrum <-
sock %>%
#PreEmphasize(alpha = 0.5) %>% # optional
#ZeroPad(lengthOut = sampleRate(sock)) %>% # again, optional
Multitaper(k = 8, nw = 4)
# print('c')
## measures for pilot1: cog, peak_full, peak_mid
## measures intended:
## cog: taken across whole range, so lower cutoff 550Hz, as K et al, to nyquist
## spread; taken across whole range, so lower cutoff 550Hz, as K et al, to nyquist [not skew and kurtosis ?]
## peak_full: across whole range, so lower cutoff 550Hz to nyquist
## peak_mid: the mid-range peak (freqM), K et al 2013
##peak_adjusted_mid: 2000 - 9000; i.e. edited ranges so that they can capture the actual high peaks of female speakers, and the possible sh productions by some male speakers. The intention is to measure realisations of /s/, which could a) be well-formed [s] but higher freq than assumed by K et al, since these do actually exist in the wild, b) other productions used for /s/, which are needed in order to demonstrate the range of possible, likely, and actual, acoustic realizations of /s/ (and also /sh/) in the wild. This is different from K et al, who seek to capture well-formed /s/, i.e. the acoustics of assumed alveolar fricative [s].
# ampdiff: ampDM-LMin, which captures difference in amplitude from lowest amp within the low range (550-3000) and highest amp in the mid range (3000-7000), K et al 2013
## ampdiff_adjusted: adjusted so mid range is 3000-9000.
## slope: from ampLmin to peaksensible rangemid. Need to do more work on script to get this working, before trying out in ISCAN.
## [NOT: Leveldiff, which was designed to track differences across the course of /s/, and doesn't mean much on its own - level = soundlevel]
## Measures: For description, discussion and justification of measures, see Protocol for sibilants revised 13 May 2020
# 1. full range peak and COG, where the range is 1000-11000Hz.
# This will capture the high PEAK for /s/, and generally capture /sh/ PEAK quite well (with the proviso that there are the odd cases of /sh/ which show a prominence >6000Hz).
corpus_data[row, "spectral_peak_full"] <- peakHz(sock_spectrum, minHz = 1000, maxHz = 11000)
corpus_data[row, "spectral_cog"] <- centroid(sock_spectrum, scale = "decibel", minHz = 1000, maxHz=11000)
# 2. a mid-frequency range peak: which serves for both /s sh/, 2000-7000Hz.
# For /sh/ the spectral maximum will generally coincide with the PEAK;
# for /s/ the spectral maximum will coincide with the PEAK for some, but not all (female) speakers.
# I would be loath to increase the range to >7000Hz, since then this really stops being anything near 'mid-frequency' range.
# print('d')
corpus_data[row, "spectral_peak_mid"] <- peakHz(sock_spectrum, minHz = 2000, maxHz = 7000)
corpus_data[row, "spectral_peak_2k8k"] <- peakHz(sock_spectrum, minHz = 2000, maxHz = 8000)
corpus_data[row, "spectral_peak_2k9k"] <- peakHz(sock_spectrum, minHz = 2000, maxHz = 9000)
# 3. spectral peak within the 2000-5000Hz range, as a general sibilant measure. This may capture the peak ~ front cavity resonance.
corpus_data[row, "spectral_peak_lower_mid"] <- peakHz(sock_spectrum, minHz = 2000, maxHz = 5000)
# 4. spread/stdev (2nd moment) - full range
corpus_data[row, "spectral_spread"] <- variance(sock_spectrum, scale = "decibel", minHz = 1000, maxHz= 11000)
# 5. ampdiff: for s and sh, measured for all sibilants, to be separated out afterwards. s_range assumes anti-resonance <3000; sh_range assumes it's lower. These are pilot measures, to look at, against troughs/peak/PEAKS. Very likely to reformulate this, given that antiresonance can shift quite a lot, by speaker, especially for /s/. Certainly, some speakers have their main antiresonance above 3000Hz in Raleigh.
# print('e')
minamp_low_s <- minAmp(sock_spectrum, scale = "dB", minHz = 1000, maxHz = 3000)
maxamp_mid_s <- maxAmp(sock_spectrum, scale = "dB", minHz= 3000, maxHz=7000)
corpus_data[row, "spectral_ampdiff_s"] <- maxamp_mid_s - minamp_low_s
minamp_low_sh <- minAmp(sock_spectrum, scale = "dB", minHz = 1000, maxHz = 2000)
maxamp_mid_sh <- maxAmp(sock_spectrum, scale = "dB", minHz= 2000, maxHz=6000)
corpus_data[row, "spectral_ampdiff_sh"] <- maxamp_mid_sh - minamp_low_sh
## 6. front slope, low-range (1000-4000Hz). This is again, just for Jane to have a look at. Proper slopes, ending on an appropriate higher upper-bound, relating to speaker peaks, will be calculated from Multitaper spectra, later.
# print('f')
spectralSlope <- function(mts, minHz = -Inf, maxHz = Inf) {
.indices <- (function(.f) {which(minHz < .f & .f < maxHz)})(frequencies(mts))
.freqs <- frequencies(mts)[.indices] %>% (function(.x) {(.x - mean(.x)) / sd(.x)})
.values <- ((function(.v) {10 * log10(.v)})(values(mts)))[.indices] %>%
(function(.x) {(.x - mean(.x)) / sd(.x)})
.spec <- data.frame(x = .freqs, y = .values)
.coeffs <- coef(lm(data = .spec, formula = y ~ x))
return(coef(lm(data = .spec, formula = y ~ x))[2])
}
corpus_data[row, "spectral_lower_slope"] <- spectralSlope(sock_spectrum, minHz = 1000, maxHz = 4000)
corpus_data[row, "spectral_cog_8k"] <- centroid(sock_spectrum, scale = "decibel", minHz = 1000, maxHz=8000)
# print('g')
#JM: store the multitaper
if (length(sock_spectrum@values) < length(mts_colnames)){
sock_spectrum@values <- c(sock_spectrum@values, rep(0,length(mts_colnames)-length(sock_spectrum@values)))
# print (sock_spectrum@values)
}
corpus_data[row,mts_colnames] <- sock_spectrum@values
#JM: how to get frequencies and values out
# frequencies <- seq(from = 0, to = sock_spectrum@nyquist, by = sock_spectrum@binWidth)
# values <- sock_spectrum@values
# print('h')
}
corpus_data
}
stopImplicitCluster()
all_multitapers$values <- corpus_data[,mts_colnames]
corpus_data <- corpus_data[,setdiff(colnames(corpus_data),mts_colnames)]
write.csv(corpus_data, paste0(args$output_dir, corpus_name,"_mts_sibilants",".csv"))
save(all_multitapers, file=paste0(args$output_dir, corpus_name,"_all_multitapers",".RData"))
}
finished_at <- date()
print (paste('started', started_at))
print (paste('finished', finished_at))