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",
  DCfix = TRUE,
  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 length (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")) {
library(ggplot2)
# We are going to load a sample noise.matrix object to demonstrate the basic usage of singleSat()
# To understand about the origin of this noise.matrix, check: ?sampleBGN
data("sampleBGN")

# View the sample noise.matrix object
sampleBGN

# Run the function
sat <- activity(sampleBGN)

# Now we can plot the results for the left channel
satLeft <- sat[,1:3]
satDim <- dim(satLeft)
numericTime <- seq(0, sum(sampleBGN@timeBins), by = sampleBGN@timeBins[1])
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",
  DCfix = TRUE
)

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 recording:

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 noise.matrix object

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 <- 60
n <- samprate * dur

# Then we generate white noise
set.seed(413)
noise <- rnorm(n)

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

# Apply fade
signal <- noise * fade

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

# Heres our artificial audio

wave

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

# See the results
bgn

# Plot background noise and soundscape power
plot(bgn)


### This is a secondary example using audio from a real soundscape
### These audios are originated from the Escutadô Project, a project
### that records the soundscapes of the brazilian semiarid
# 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)

# Here's the result
bgn

# Plot background noise and soundscape power values
plot(bgn)

# Plot the two indices against each other
plot(bgn@values$left$BGN$BGN1, bgn@values$left$POW$POW1,
     xlab = "BGN (dB)", ylab = "POW (dB)", pch = 16)

# Now lets test and plot their correlation
BGNPOWlm <- lm(bgn@values$left$BGN$BGN1~bgn@values$left$POW$POW1)
summary(BGNPOWlm)
abline(lm(bgn@values$left$BGN$BGN1~bgn@values$left$POW$POW1), col = "red")

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",
  DCfix = TRUE,
  powthr = 10,
  bgnthr = 0.8,
  beta = TRUE,
  backup = NULL
)

Arguments

Details

In this function, we only generate activity matrices for an directory 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., as ~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

See Also

soundSat() and soundMat() to get saturation values. Also, check satBackup() if you are working with larger datasets and want some safety.

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)

  }

noise.matrix class

Description

This class object is generated by the bgNoise function. It can be read by soundSat.
Methods: show() and plot()

Value

A noise.matrix object

Slots

values

a list containing the values of BGN and POW. Indices are separated in individual data.frames by channel

timeBins

numeric vector containing the duration of each time bin

sampRate

single numeric string containing the audio's sampling rate

wl

single numeric string containing the spectrogram's window length

channel

single character string containing the channel or channels used to calculated the indices

See Also

bgNoise()

Plot noise.matrix objects

Description

Plot noise.matrix objects

Usage

## S4 method for signature 'noise.matrix,ANY'
plot(
  x,
  channel = NULL,
  bin = 1,
  index = c("BGN", "POW"),
  nbreaks = 5,
  yunit = c("hz", "khz"),
  main = NULL,
  xlab = "dB",
  ylab = "Frequency",
  col = c("blue", "red"),
  type = "p",
  draw0 = TRUE,
  box = TRUE,
  axes = TRUE,
  annotate = TRUE,
  ...
)

Arguments

Details

This is a method to quickly plot the results of bgNoise. This calls the helper function plotBGN, which is not meant to be used or seen by the user.

Helper function to plot noise.matrix objects

Description

Helper function to plot noise.matrix objects

Usage

plotBGN(
  x,
  channel,
  bin,
  index,
  nbreaks,
  yunit,
  main,
  xlab,
  ylab,
  col,
  type,
  draw0,
  box,
  axes,
  annotate,
  ...
)

Arguments

Details

This is a helper function called by ⁠plot,Wave,missing-method⁠. This is not intended to be seen or used by the user!

Sample noise.matrix object

Description

This is a sample noise.matrix object generated by the bgNoise function. Used in the examples of: singleSat and activity
The original sound file was obtained from the Escutadô Project, an initiative aimed at recording soundscapes across the Brazilian semiarid region. The audio was recorded by an autonomous recorder (SM4) attached approximately 4'11" inches above ground on a metal rod installed in a Black Jurema (Mimosa tenuiflora) forest in Federal Rural University of the Semi-Arid (UFERSA), located in Mossoró, Rio Grande do Norte, Brazil. The recording was taken at midnight (00:00) and is 3 minutes long, with a sampling rate of 48 kHz and gain levels of 16 dB on the left channel and 10 dB on the right channel.

Usage

sampleBGN

Format

sampleBGN

A noise.matrix object with:

@values

Background Noise and Soundscape Power values

@timeBins

Duration in seconds of each time bin

@sampRate

The recording's sample rate

@wl

The window length used to generate the spectrogram used to calculate the metrics

@channel

The channel or channels of the recording

Source

https://zenodo.org/records/17243660

Backup for Ruido's functions

Description

This function offers 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 file. However you may manually change them by editing the file (not recommended).

Usage

satBackup(backup)

Arguments

Value

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

Examples

## Not run: 
# It's impossible to demonstrate this function's intended use due to it's nature
# 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",
  DCfix = TRUE,
  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}

Value

A list 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

See Also

soundSat() and soundMat() to work with multiple audio files and activity() to get only the activity matrix

Examples

# First example: Using a Ruido noise.matrix object
# We are going to load a sample noise.matrix object to demonstrate the basic usage of singleSat()
# To understand about the origin of this noise.matrix, check: ?sampleBGN
data("sampleBGN")

# View the sample noise.matrix object
sampleBGN

# Run the function
SAT <- singleSat(sampleBGN)

# View the results
SAT

# Now lets plot our results to see the dynamics of soundscape saturation by minute
maxV <- max(unlist(SAT))
minV <- min(unlist(SAT))

plot(x = c(1, 3), y = c(minV, maxV), type = "n",
     xlab = "Minute", ylab = "Soundscape Saturation (%)", xaxt = "n")
lines(x = 1:3, SAT$left, col = "#1ECBE1", type = "b", pch = 16)
axis(1, at = 1:3)
lines(x = 1:3, SAT$right, col = "#E1341E", type = "b", pch = 16)
legend("topright", legend = c("Left", "Right"), col = c("#1ECBE1", "#E1341E"), lty = 1, pch = 16)

# Second example: Using a tuneR Wave-class object
# Lets produce an artificial audio with the tuneR package to demonstrate that
# the function can also read Wave-class objects (This is the same object used in
# the example of bgNoise!)

library(tuneR)

oldpar <- par(no.readonly = TRUE)

# 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$mono, 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)


# Third example: Reading a file directly
# Lets begin by loading an audio from the online Zenodo library and
# read it directly with the function
# 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(unlist(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",
  DCfix = TRUE,
  powthr = c(5, 20, 1),
  bgnthr = c(0.5, 0.9, 0.05),
  beta = TRUE,
  backup = NULL
)

Arguments

Details

Check soundSat() to see how the indices are calculated.

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., as ~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

See Also

soundSat() to get only the threshold with the most normal distribution and multActivity() to generate only activity matrices. Also, check satBackup() if you are working with larger datasets and want some safety.

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",
  DCfix = TRUE,
  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., as ~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

See Also

soundMat() to get saturation for ALL thresholds and multActivity() to get only activity values. Also, check satBackup() if you are working with bigger datasets.

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)