Acoustic Activity Matrix

Description

Calculate the Acoustic Activity Matrix using the methodology proposed in Burivalova 2018

Usage

activity(
  soundfile,
  channel = "stereo",
  timeBin = 60,
  dbThreshold = -90,
  targetSampRate = NULL,
  wl = 512,
  window = signal::hamming(wl),
  overlap = ceiling(length(window)/2),
  histbreaks = "FD",
  powthr = 10,
  bgnthr = 0.8,
  beta = TRUE
)

Arguments

Details

To calculate the activity matrix, we use the methodology proposed by Burivalova 2018. We begin by applying the following formula to each time bin of the recording:

a_{mf} = 1\ if (BGN_{mf} > \theta_{1})\ or\ (POW_{mf} > \theta_{2});\ otherwise,\ a_{mf} = 0,

Where \theta_{1} equals the threshold of BGN values and \theta_{2} equals the threshold of dB values. We set 1 to active and 0 to inactive frequency windows.

Value

This function returns a 0 and 1 matrix containing the activity for all time bins of the inputted file. The matrix's number of rows will equal to half the set window lenght (wl) and number of columns will equal the number of bins. Cells with the value of 1 represent the acoustically active frequency of a bin.

References

Burivalova, Z., Towsey, M., Boucher, T., Truskinger, A., Apelis, C., Roe, P., & Game, E. T. (2018). Using soundscapes to detect variable degrees of human influence on tropical forests in Papua New Guinea. Conservation Biology, 32(1), 205-215. https://doi.org/10.1111/cobi.12968

Examples

if (require("ggplot2")) {
### Generating an artificial audio for the example
## For this example we'll generate a sweep in a noisy soundscape
library(tuneR)
library(ggplot2)

# Define parameters for the artificial audio
samprate <- 12050
dur <- 60
n <- samprate * dur

# White noise
set.seed(413)
noise <- rnorm(n)

# Linear fade-out envelope
fade <- seq(1, 0, length.out = n)

# Apply fade
signal <- noise * fade

# Create Wave object
wave <- Wave(
  left = signal,
  samp.rate = samprate,
  bit = 16
)

# Running singleSat() on the artificial audio
time <- 10
sat <- activity(wave, timeBin = time)

# Now we can plot the results
satDim <- dim(sat)
numericTime <- seq(0, dur, by = time)
labels <- paste0(numericTime[-length(numericTime)], "-", numericTime[-1], "s")

satDF <- data.frame(BIN = rep(paste0("BIN", seq(satDim[2])), each = satDim[1]),
                    WIN = rep(seq(satDim[1]), satDim[2]),
                    ACT = factor(c(sat), levels = c(0,1)))

ggplot(satDF, aes(x = BIN, y = WIN, fill = ACT)) +
  geom_tile() +
  theme_bw() +
  scale_fill_manual(values = c("white", "black")) +
  scale_y_continuous(expand = c(0,0)) +
  scale_x_discrete(expand = c(0,0), labels = labels) +
  labs(x = "Time Bin", y = "Spectral Window") +
  guides(fill = guide_legend(title = "Activity"))

}

Background Noise and Soundscape Power Index

Description

Calculate the Background Noise and Soundscape Power values of a single audio using the methodology proposed in Towsey 2017

Usage

bgNoise(
  soundfile,
  channel = "stereo",
  timeBin = 60,
  dbThreshold = -90,
  targetSampRate = NULL,
  wl = 512,
  window = signal::hamming(wl),
  overlap = ceiling(length(window)/2),
  histbreaks = "FD"
)

Arguments

Details

Background Noise (BGN) is an acoustic metric that measures the most common continuous baseline level of acoustic energy in a frequency window and in a time bin. It was developed by Towsey 2017 using the Lamel et al 1981 algorithm. The metric is calculated by taking the modal value of intensity values in temporal bin c in frequency window f of a reconding:

BGN_{f} = mode(dB_{cf})

Soundscape Power represents a measure of signal-to-noise ratio. It measures the relation of BGN to the loudest intensities in temporal bin c in frequency window f:

POW_{f} = max(dB_{cf}) - BGN_{cf}

This mean we'll have a value of BGN and POW to each frequency window of a recording.

Value

This function returns a list containing five objects. The first object (values) contain the values of BGN and POW. The second object (timeBins) contains the durations of each time bin analysed. The third object (sampRate) contains the audio's sampling rate. The fourth and last object (channel) contains the channels used for the calculation of the metric.

References

Towsey, M. W. (2017). The calculation of acoustic indices derived from long-duration recordings of the natural environment. In eprints.qut.edu.au. https://eprints.qut.edu.au/110634/
Lamel, L., Rabiner, L., Rosenberg, A., & Wilpon, J. (1981). An improved endpoint detector for isolated word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29(4), 777-785 https://doi.org/10.1109/TASSP.1981.1163642

Examples

### For our main example we'll create an artificial audio with
### white noise to test its Background Noise
# We'll use the package tuneR
library(tuneR)

# Define the audio sample rate, duration and number of samples
sampRate <- 12050
dur <- 59
sampN <- sampRate * dur

# Then we Ggenerate the white noise for our audio and apply FFT
set.seed(413)
ruido <- rnorm(sampN)
spec <- fft(ruido)

# Now we create a random spectral envelope for the audio and apply the spectral envelope
nPoints <- 50
env <- runif(nPoints)
env <- approx(env, n=nPoints)$y
specMod <- spec * env

# Now we invert the FFT back to into a time waveform and normalize and convert to Wave
out <- Re(fft(specMod, inverse=TRUE)) / sampN
wave <- Wave(left = out, samp.rate = sampRate, bit = 16)
wave <- normalize(wave, unit = "16")

# Here's our artificial audio
wave

# Running the bgNoise function with all the default arguments
bgn <- bgNoise(wave)

# Print the results
head(bgn$values$mono$BGN)
head(bgn$values$mono$POW)

# Plotting background noise and soundscape profile for the first minute of the recording
par(mfrow = c(2,2))
plot(x = bgn$values$mono$BGN$BGN1, y = seq(1,bgn$sampRate, length.out = 256),
     xlab = "Background Noise (dB)", ylab = "Frequency (hz)",
     main = "BGN by Frequency",
     type = "l")
plot(x = bgn$values$mono$POW$POW1, y = seq(1,bgn$sampRate, length.out = 256),
     xlab = "Soundscape Power (dB)", ylab = "Frequency (hz)",
     main = "POW by Frequency",
     type = "l")
plot(bgn$values$mono$BGN$BGN1~bgn$values$mono$POW$POW1, pch = 16,
     xlab = "Soundscape Power (dB)", ylab = "Background Noise (dB)",
     main = "BGN~POW")
hist(bgn$values$mono$BGN$BGN1, main = "Histogram of BGN distribution",
      xlab = "Background Noise (BGN)")


oldpar <- par(no.readonly = TRUE)
### This is a secondary example using audio from a real soundscape
### These audios are originated from the Escutadô Project
# Getting audiofile from the online Zenodo library
dir <- paste(tempdir(), "forExample", sep = "/")
dir.create(dir)
rec <- paste0("GAL24576_20250401_", sprintf("%06d", 0), ".wav")
recDir <- paste(dir, rec , sep = "/")
url <- paste0("https://zenodo.org/records/17575795/files/",
              rec,
              "?download=1")

# Downloading the file, might take some time denpending on your internet
download.file(url, destfile = recDir, mode = "wb")

# Running the bgNoise function with all the default arguments
bgn <- bgNoise(recDir)

# Print the results
head(bgn$values$left$BGN)
head(bgn$values$left$POW)

# Plotting background noise and soundscape profile for the first minute of the recording
par(mfrow = c(2, 2))
plot(
  x = bgn$values$left$BGN$BGN1,
  y = seq(1, bgn$sampRate, length.out = 256),
  xlab = "Background Noise (dB)",
  ylab = "Frequency (hz)",
  main = "BGN by Frequency",
  type = "l"
)
plot(
  x = bgn$values$left$POW$POW1,
  y = seq(1, bgn$sampRate, length.out = 256),
  xlab = "Soundscape Power (dB)",
  ylab = "Frequency (hz)",
  main = "POW by Frequency",
  type = "l"
)
plot(
  bgn$values$left$BGN$BGN1 ~ bgn$values$left$POW$POW1,
  pch = 16,
  xlab = "Soundscape Power (dB)",
  ylab = "Background Noise (dB)",
  main = "BGN~POW in left ear"
)
plot(
  bgn$values$right$BGN$BGN1 ~ bgn$values$right$POW$POW1,
  pch = 16,
  xlab = "Soundscape Power (dB)",
  ylab = "Background Noise (dB)",
  main = "BGN~POW in right ear"
)

unlink(dir, recursive = TRUE)
par(oldpar)

Multiple Acoustic Activity Matrix

Description

Calculate the Acoustic Activity Matrix used in the the calculation of Soundscape Saturation using Burivalova 2018 methodology for a set of recordings

Usage

multActivity(
  soundpath,
  channel = "stereo",
  timeBin = 60,
  dbThreshold = -90,
  targetSampRate = NULL,
  wl = 512,
  window = signal::hamming(wl),
  overlap = ceiling(length(window)/2),
  histbreaks = "FD",
  powthr = 10,
  bgnthr = 0.8,
  beta = TRUE,
  backup = NULL
)

Arguments

Details

We generate an activity matrix using Burivalova 2018 methodology. For each time bin of the recording we apply the following formula:

a_{mf} = 1\ if (BGN_{mf} > \theta_{1})\ or\ (POW_{mf} > \theta_{2});\ otherwise,\ a_{mf} = 0,

Where \theta_{1} is the threshold of BGN values and \theta_{2} is a threshold of dB values. 1 = active and 0 = inactive.

If backup is set to a valid directory, a file named "SATBACKUP.RData" is saved after every batch of five processed files. If the function execution is interrupted (e.g., manual termination, an R session crash, or a system shutdown), this backup file can be passed to satBackup() (e.g., ~path/SATBACKUP.RData) to resume the original process. Once a backup is created, all arguments and file paths must remain unchanged, unless they are manually modified within the .RData object.

Value

A list containing five objects. The first and second objects (powthresh and bgnthresh) are the threshold values inputted as arguments into the function. The third (info) contains the following variables from every audio file: PATH, AUDIO, CHANNEL, DURATION, BIN, SAMPRATE.. The fourth object (values) contains a matrix with the the values of activity for each bin of each recording and the size of the bin in seconds. The fifth contains a list with errors that occurred with specific files during the function.

References

Burivalova, Z., Towsey, M., Boucher, T., Truskinger, A., Apelis, C., Roe, P., & Game, E. T. (2018). Using soundscapes to detect variable degrees of human influence on tropical forests in Papua New Guinea. Conservation Biology, 32(1), 205-215. https://doi.org/10.1111/cobi.12968

Examples

if (require("ggplot2") & require("patchwork")) {
  ### Generating an artificial audio for the example
  ## For this example we'll generate a sweep in a noisy soundscape
  library(ggplot2)
  library(patchwork)

  ### Downloading audiofiles from public Zenodo library
  dir <- paste0(tempdir(), "/forExamples")
  dir.create(dir)
  recName <- paste0("GAL24576_20250401_", sprintf("%06d", seq(0, 200000, by = 50000)), ".wav")
  recDir <- paste(dir, recName, sep = "/")

  for (rec in recName) {
    print(rec)
    url <- paste0("https://zenodo.org/records/17575795/files/",
                  rec,
                  "?download=1")
    download.file(url,
                  destfile = paste(dir, rec, sep = "/"),
                  mode = "wb")
  }

  time <- sapply(strsplit(recName, "_"), function(x)
    paste(substr(x[3], 1, 2), substr(x[3], 3, 4), substr(x[3], 5, 6), sep = ":"))
  date <- sapply(strsplit(recName, "_"), function(x)
    paste(substr(x[2], 1, 4), substr(x[2], 5, 6), substr(x[2], 7, 8), sep = "-"))

  dateTime <- as.POSIXct(paste(date, time))

  timeLabels <- time[c(1, 7, 13, 19, 24)]
  timeBreaks <- as.character(dateTime[c(1, 7, 13, 19, 24)])

  breaks <- round(c(1, cumsum(rep(256 / 6, 6))))

  ### Running the function
  act <- multActivity(dir)

  plotN <- 1

  sDim <- dim(act$values)

  sampRate <- act$info$SAMPRATE[1]
  kHz <- cumsum(c(0, rep(sampRate / 6, 6))) / 1000

  plotList <- list()

  for (cha in c("left", "right")) {
    actCurrent <- act$values[, act$info$CHANNEL == cha]
    actCurrentDF <- data.frame(
      TIME = as.character(rep(dateTime, each = sDim[1])),
      SPEC = rep(seq(sDim[1]), sDim[2]),
      VAL = factor(c(unlist(actCurrent)), levels = c(0, 1))
    )

    plotList[[plotN]] <- ggplot(actCurrentDF, aes(x = TIME, y = SPEC, fill = VAL)) +
      geom_tile() +
      theme_classic() +
      scale_y_continuous(expand = c(NA, NA),
                         labels = kHz,
                         breaks = breaks) +
      scale_x_discrete(expand = c(0, 0),
                       labels = time) +
      scale_fill_manual(values = c("white", "black"),
                        labels = c("Inactive", "Active")) +
      guides(fill = guide_legend(title = "Acoustic Activity")) +
      labs(
        x = "Time of Day",
        y = "Frequency (kHz)",
        title = paste("Acoustic Activity in the", cha, "channel")
      )

    plotN <- plotN + 1

  }

  plotList[[1]] + plotList[[2]] + plot_layout(guide = "collect")

  unlink(recDir)
  unlink(dir)

  }

Backup for Ruido's functions

Description

This function is a way to continue an unfinished process of the soundSat(), soundMat() or multActivity() functions through a backup file. Arguments can't be inputted nor changed since the function will automatically load them from the .RData files. However you may manually change them (not recomended)

Usage

satBackup(backup)

Arguments

Value

This functions returns the same output of soundSat(), soundMat() or multActivity()

Examples

## Not run: 
# It's impossible to create a functioning example since you would have to manually stop the process
# However, here is how this function is used:
## This example will load an entire day of audios to your computer, so beware.

### Downloading audiofiles from public Zenodo library
dir <- paste(tempdir(), "forExample", sep = "/")
dir.create(dir)
recName <- paste0("GAL24576_20250401_", sprintf("%06d", seq(0, 230000, by = 10000)),".wav")
recDir <- paste(dir, recName, sep = "/")

for(rec in recName) {
  print(rec)
  url <- paste0("https://zenodo.org/records/17575795/files/", rec, "?download=1")
  download.file(url, destfile = paste(dir, rec, sep = "/"), mode = "wb")
}

sat <- soundSat(dir, backup = dir)

# Now pretend the process was interrupted (manually/your R crashed/your computer turned off)
# We get the backup file

list.files(dir)
backupDir <- paste(dir, "SATBACKUP.RData", sep = "/")

# To recall the backup you simply:

satB <- satBackup(backupDir)

head(satB$values)

unlink(dir, recursive = TRUE)

## End(Not run)

Single Soundscape Saturation Index

Description

Single Soundscape Saturation Index

Usage

singleSat(
  soundfile,
  channel = "stereo",
  timeBin = 60,
  dbThreshold = -90,
  targetSampRate = NULL,
  wl = 512,
  window = signal::hamming(wl),
  overlap = ceiling(length(window)/2),
  histbreaks = "FD",
  powthr = 10,
  bgnthr = 0.8,
  beta = TRUE
)

Arguments

Details

Soundscape Saturation (SAT) is a measure of the proportion of frequency bins that are acoustically active in a determined window of time. It was developed by Burivalova et al. 2018 as an index to test the acoustic niche hypothesis. To calculate this function, first we need to generate an activity matrix for each time bin of your recording with the following formula:

a_{mf} = 1\ if (BGN_{mf} > \theta_{1})\ or\ (POW_{mf} > \theta_{2});\ otherwise,\ a_{mf} = 0,

Where \theta_{1} is the threshold of BGN values and \theta_{2} is a threshold of dB values. Since we define a single threshold for both in this function, we don't have to worry about generating a saturation value for many different combinations. For the selected threshold a soundscape saturation measure will be taken with the following formula:

S_{m} = \frac{\sum_{f = 1}^N a_{mf}}{N}

Since this is analyzing the soundscape saturaion of a single file, no normality tests will be done.

Value

A vector containing the saturation values for all time bins of the inputted file

References

Burivalova, Z., Towsey, M., Boucher, T., Truskinger, A., Apelis, C., Roe, P., & Game, E. T. (2018). Using soundscapes to detect variable degrees of human influence on tropical forests in Papua New Guinea. Conservation Biology, 32(1), 205-215. https://doi.org/10.1111/cobi.12968

Examples

oldpar <- par(no.readonly = TRUE)

### Generating an artificial audio for the example
## For this example we'll generate a sweep in a noisy soundscape
library(tuneR)

# Define parameters for the artificial audio
samprate <- 12050
dur <- 59
n <- samprate * dur

# White noise
set.seed(413)
noise <- rnorm(n)

# Linear fade-out envelope
fade <- seq(1, 0, length.out = n)

# Apply fade
signal <- noise * fade

# Create Wave object
wave <- Wave(
  left = signal,
  samp.rate = samprate,
  bit = 16
)

# Running singleSat() on the artificial audio
sat <- singleSat(wave, timeBin = 10)

# Now we can plot the results
# In the left we have a periodogram and in the right saturaion values
# along one minute
par(mfrow = c(1,2))
image(periodogram(wave, width = 8192, normalize = FALSE), xlab = "Time (s)",
ylab = "Frequency (hz)", axes = FALSE)
axis(1, labels = seq(0,60, 10), at = seq(0,7e5,length.out = 7))
axis(2)
plot(sat, xlab = "Time (s)", ylab = "Soundscape Saturation (%)",
type = "b", pch = 16, axes = FALSE)
axis(1, labels = paste0(c("0-10","10-20","20-30","30-40","40-50","50-59"),
"s"), at = 1:6)
axis(2)

par(oldpar)


# Getting audiofile from the online Zenodo library
dir <- paste(tempdir(), "forExample", sep = "/")
dir.create(dir)
rec <- paste0("GAL24576_20250401_", sprintf("%06d", 0),".wav")
recDir <- paste(dir,rec , sep = "/")
url <- paste0("https://zenodo.org/records/17575795/files/", rec, "?download=1")

# Downloading the file, might take some time denpending on your internet
download.file(url, destfile = recDir, mode = "wb")

# Now we calculate soundscape saturation for both sides of the recording
sat <- singleSat(recDir)

# Printing the results
print(sat)

barplot(sat, col = c("darkgreen", "red"),
       names.arg = c("Left", "Right"), ylab = "Soundscape Saturation (%)")

unlink(dir, recursive = TRUE)

Soundscape Saturation Matrix

Description

Get the Soundscape Saturation matrix with all threshold combinations instead of the combination with the most normal distribution

Usage

soundMat(
  soundpath,
  channel = "stereo",
  timeBin = 60,
  dbThreshold = -90,
  targetSampRate = NULL,
  wl = 512,
  window = signal::hamming(wl),
  overlap = ceiling(length(window)/2),
  histbreaks = "FD",
  powthr = c(5, 20, 1),
  bgnthr = c(0.5, 0.9, 0.05),
  beta = TRUE,
  backup = NULL
)

Arguments

Details

Soundscape Saturation (SAT) is a measure of the proportion of frequency bins that are acoustically active in a determined window of time. It was developed by Burivalova et al. 2018 as an index to test the acoustic niche hypothesis. To calculate this function, first we need to generate an activity matrix for each time bin of your recording with the following formula:

a_{mf} = 1\ if (BGN_{mf} > \theta_{1})\ or\ (POW_{mf} > \theta_{2});\ otherwise,\ a_{mf} = 0,

Where \theta_{1} is the threshold of BGN values and \theta_{2} is a threshold of dB values. Since we define a interval for both the threshold, this means that an activity matrix will be generated for each bin of each recording. For each combination of threshold a SAT measure will be taken with the following formula:

S_{m} = \frac{\sum_{f = 1}^N a_{mf}}{N}

After these equations are done, we check every threshold combination for normality and pick the combination that yields the most normal distribution of saturation values.

If backup is set to a valid directory, a file named "SATBACKUP.RData" is saved after every batch of five processed files. If the function execution is interrupted (e.g., manual termination, an R session crash, or a system shutdown), this backup file can be passed to satBackup() (e.g., ~path/SATBACKUP.RData) to resume the original process. Once a backup is created, all arguments and file paths must remain unchanged, unless they are manually modified within the .RData object.

Value

A list containing three objects. The first (info) contains the following variables from every audio file: PATH, AUDIO, CHANNEL, DURATION, BIN, SAMPRATE. The second (values) contains saturation values from all possible threshold combinations. The third (errors) contains the error messages and the paths to the files that returned an error during processing.

References

Burivalova, Z., Towsey, M., Boucher, T., Truskinger, A., Apelis, C., Roe, P., & Game, E. T. (2018). Using soundscapes to detect variable degrees of human influence on tropical forests in Papua New Guinea. Conservation Biology, 32(1), 205-215. https://doi.org/10.1111/cobi.12968

Examples

oldpar <- par(no.readonly = TRUE)
### Downloading audiofiles from public Zenodo library
dir <- paste(tempdir(), "forExample", sep = "/")
dir.create(dir)
recName <- paste0("GAL24576_20250401_", sprintf("%06d", seq(0, 200000, by = 50000)), ".wav")
recDir <- paste(dir, recName, sep = "/")

for (rec in recName) {
  print(rec)
  url <- paste0("https://zenodo.org/records/17575795/files/",
                rec,
                "?download=1")
  download.file(url, destfile = paste(dir, rec, sep = "/"), mode = "wb")
}

### Running the function
sat <- soundMat(dir)

### Plotting results
sides <- sat$info$CHANNEL

thresholds <- colnames(sat$values)
split <- strsplit(thresholds, "/")

shapNorm <- apply(sat$values, 2, function(x)

  if (var(x) == 0) {
    0
  } else {
    shapiro.test(x)$statistic
  })

shapPos <- which.max(shapNorm)

par(mfrow = c(3, 2))

plot(
  sat$values[sides == "left", 1],
  main = paste0("POW = ", split[[1]][1], "dB | BGN = ", split[[1]][2], "%"),
  type = "b",
  ylim = c(0,1),
  xlab = "Time Index", ylab = "Soundsacpe Saturation (%)", col = "goldenrod"
)
points(sat$values[sides == "right", 1], col = "maroon", type = "b")

hist(sat$values[,1], main = paste("Histogram of POW = ", split[[1]][1],
"dB | BGN = ", split[[1]][2], "%"), xlab = "Soundscape Saturation (%)")

plot(
sat$values[sides == "left", 144],
main = paste0("POW = ", split[[144]][1], "dB | BGN = ", split[[144]][2], "%"),
type = "b",
ylim = c(0,1),
xlab = "Time Index", ylab = "Soundsacpe Saturation (%)", col = "goldenrod"
)
points(sat$values[sides == "right", 144], col = "maroon", type = "b")

hist(sat$values[,144], main = paste("Histogram of POW = ", split[[144]][1],
"dB | BGN = ", split[[144]][2], "%"), xlab = "Soundscape Saturation (%)")

plot(
  sat$values[sides == "left", shapPos],
  main = paste0(
    "POW = ",
    split[[shapPos]][1],
    "dB | BGN = ",
    split[[shapPos]][2],
    "%",
    "\nshapiro.test. statistic (W): ",
    which.max(shapNorm)
  ),
  type = "b",
  ylim = c(0,1),
  xlab = "Time Index", ylab = "Soundsacpe Saturation (%)", col = "goldenrod"
)
points(sat$values[sides == "right", shapPos], col = "maroon", type = "b")
hist(sat$values[,shapPos], main = paste("Histogram of POW = ",
split[[shapPos]][1], "dB | BGN = ", split[[shapPos]][2], "%"),
xlab = "Soundscape Saturation (%)")

unlink(dir, recursive = TRUE)
par(oldpar)

Soundscape Saturation Index

Description

Calculate Soundscape Saturation for a combination of recordings using the methodology proposed in Burivalova 2018

Usage

soundSat(
  soundpath,
  channel = "stereo",
  timeBin = 60,
  dbThreshold = -90,
  targetSampRate = NULL,
  wl = 512,
  window = signal::hamming(wl),
  overlap = ceiling(length(window)/2),
  histbreaks = "FD",
  powthr = c(5, 20, 1),
  bgnthr = c(0.5, 0.9, 0.05),
  normality = "ad.test",
  beta = TRUE,
  backup = NULL
)

Arguments

Details

Soundscape Saturation (SAT) is a measure of the proportion of frequency bins that are acoustically active in a determined window of time. It was developed by Burivalova et al. 2018 as an index to test the acoustic niche hypothesis. To calculate this function, first we need to generate an activity matrix for each time bin of your recording with the following formula:

a_{mf} = 1\ if (BGN_{mf} > \theta_{1})\ or\ (POW_{mf} > \theta_{2});\ otherwise,\ a_{mf} = 0,

Where \theta_{1} is the threshold of BGN values and \theta_{2} is a threshold of dB values. Since we define a interval for both the threshold, this means that an activity matrix will be generated for each bin of each recording. For each combination of threshold a SAT measure will be taken with the following formula:

S_{m} = \frac{\sum_{f = 1}^N a_{mf}}{N}

After these equations are done, we check every threshold combination for normality and pick the combination that yields the most normal distribution of saturation values.

If backup is set to a valid directory, a file named "SATBACKUP.RData" is saved after every batch of five processed files. If the function execution is interrupted (e.g., manual termination, an R session crash, or a system shutdown), this backup file can be passed to satBackup() (e.g., ~path/SATBACKUP.RData) to resume the original process. Once a backup is created, all arguments and file paths must remain unchanged, unless they are manually modified within the .RData object.

Value

A list containing five objects. The first and second objects (powthresh and bgnthresh) are the threshold values that yielded the most normal distribution of saturation values using the normality test set by the user. The third (normality) contains the statitics values of the normality test that yielded the most normal distribution. The fourth object (values) contains a data.frame with the the values of saturation for each bin of each recording and the size of the bin in seconds. The fifth contains a data.frame with errors that occurred with specific files during the function.

References

Burivalova, Z., Towsey, M., Boucher, T., Truskinger, A., Apelis, C., Roe, P., & Game, E. T. (2018). Using soundscapes to detect variable degrees of human influence on tropical forests in Papua New Guinea. Conservation Biology, 32(1), 205-215. https://doi.org/10.1111/cobi.12968

Examples

### Downloading audiofiles from public Zenodo library
dir <- paste(tempdir(), "forExample", sep = "/")
dir.create(dir)
recName <- paste0("GAL24576_20250401_", sprintf("%06d", seq(0, 200000, by = 50000)),".wav")
recDir <- paste(dir, recName, sep = "/")

for(rec in recDir) {
 print(rec)
 url <- paste0("https://zenodo.org/records/17575795/files/", basename(rec), "?download=1")
 download.file(url, destfile = rec, mode = "wb")
}

### Running the function
sat <- soundSat(dir)

### Preparing the plot
timeSplit <- strsplit(sat$values$AUDIO, "_")
sides <- sat$values$CHANNEL
date <- sapply(timeSplit, function(x)
  x[2])
time <- sapply(timeSplit, function(x)
  substr(x[3],1,6))
datePos <- paste(substr(date,1,4), substr(date,5,6), substr(date,7,8), sep = "-")
timePos <- paste(substr(time,1,2), substr(time,3,4), substr(time,5,6), sep = ":")
dateTime <- as.POSIXct(paste(datePos, timePos), format = "%Y-%m-%d %H:%M:%OS")
leftEar <- data.frame(SAT = sat$values$SAT[sides == "left"], HOUR = dateTime[sides == "left"])
rightEar <- data.frame(SAT = sat$values$SAT[sides == "right"], HOUR = dateTime[sides == "right"])

### Plotting results

plot(SAT~HOUR, data = leftEar, ylim = c(range(sat$values$SAT)),
col = "darkgreen", pch = 16,
     ylab = "Soundscape Saturation (%)", xlab = "Time of Day", type = "b")
points(SAT~HOUR, data = rightEar, ylim = c(range(sat$values$SAT)),
col = "red", pch = 16, type = "b")
legend("bottomright", legend = c("Left Ear", "Right Ear"),
       col = c("darkgreen", "red"), lty = 1)

unlink(dir, recursive = TRUE)