Package {Ruido}

Spectral Acoustic Complexity Index

Description

Calculate the Acoustic Complexity Index values of a single audio using the methodology proposed in Pieretti, et al. 2011

Usage

ACIspec(
  soundfile,
  channel = "stereo",
  timeBin = 60,
  j = 5,
  targetSampRate = NULL,
  wl = 512,
  window = signal::hamming(wl),
  overlap = ceiling(length(window)/2)
)

Arguments

Details

The Acoustic Complexity Index (ACI) quantifies the average proportional change in spectral amplitude between adjacent time steps across frequency bins. Because biological sounds, particularly bird vocalizations, often exhibit rapid and irregular amplitude fluctuations over time, ACI captures this temporal variability as a proxy for acoustic complexity.

In Ruido, ACI is computed independently within each time bin. Within a time bin, the signal is further subdivided into smaller temporal segments, here referred to as cluster intervals j.

For a given frequency bin f_l, acoustic intensity values I_k are evaluated across consecutive time steps k within each cluster interval j. The absolute differences between adjacent time steps are calculated as:

d_k = |I_k - I_{k+1}|

These differences are summed within each cluster interval:

D_j = \sum_{k = 1}^{N} d_k

where N is the number of time steps \Delta t_k in interval j. The ACI for each cluster interval is then:

ACI_j = \frac{D_j}{\sum_{k = 1}^{N} I_k}

where \sum_{k = 1}^{N} I_k is the total acoustic intensity within the same interval.

For each frequency bin f_l, ACI values are summed across all cluster intervals within the time bin:

ACI_{f_l} = \sum_{j = 1}^{m} ACI_j

where m is the number of cluster intervals in the time bin.

The result is a frequency-resolved representation of ACI for each time bin, rather than a single scalar value for the entire recording.

In the original formulation (Pieretti et al., 2011), ACI is further summed across all frequency bins:

ACI_{tot} = \sum_{l = 1}^{q} ACI_{f_l}

where q is the total number of frequency bins. This final aggregation step is not performed in this package.

Value

This function returns a noise.matrix object.

References

Pieretti, N., Farina, A., & Morri, D. (2011). A new methodology to infer the singing activity of an avian community: The Acoustic Complexity Index (ACI). Ecological Indicators, 11(3), 868–873. https://doi.org/10.1016/j.ecolind.2010.11.005

See Also

ENTspec() to calculate Spectral Entropy and bgNoise() to calculate Background Noise and Soundscape Power.

Examples

### This is an 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 ACIspec function with all the default arguments
aci = ACIspec(recDir)

# Here's the result
aci

# Plot ACI values
plot(aci)

Spectral Temporal Entropy Index

Description

Calculate the Temporal Entropy values of a single audio using the methodology proposed in Towsey, et al. 2014

Usage

ENTspec(
  soundfile,
  channel = "stereo",
  timeBin = 60,
  targetSampRate = NULL,
  wl = 512,
  window = signal::hamming(wl),
  overlap = ceiling(length(window)/2)
)

Arguments

Details

The Temporal Entropy (ENT) quantifies how concentrated or dispersed acoustic energy is over time within each frequency bin. Unlike indices that track frame-to-frame change (e.g. ACI), ENT treats the distribution of energy across an entire time bin as a probability mass function and measures its Shannon entropy, capturing whether energy is spread evenly through time (high entropy, low concentration) or concentrated into brief pulses (low entropy, high concentration).

In Ruido, ENT is computed independently within each time bin, using every time step of the spectrogram in that bin.

For a given frequency bin f, the squared amplitude values I_t across all time steps t within the time bin are normalized to unit area, producing a probability mass function:

pmf_{f,t} = \frac{I_t^2}{\sum_{t = 1}^{N} I_t^2}

where N is the number of time steps in the time bin. The Shannon entropy of this distribution is then calculated as:

H[f] = \frac{-\sum_{t = 1}^{N} pmf_{f,t} \times \log_2(pmf_{f,t})}{\log_2 N}

To express the result as an intuitive measure of energy concentration rather than dispersion, ENT is calculated as the complement of H:

ENT[f] = 1 - H[f]

The result is a frequency-resolved representation of ENT for each time bin, rather than a single scalar value for the entire recording. Values close to 1 indicate energy concentrated in few time steps (e.g. transient calls or pulses), while values close to 0 indicate energy spread evenly across the time bin (e.g. steady background noise).

Value

This function returns a noise.matrix object.

References

Towsey, M., Wimmer, J., Williamson, I., & Roe, P. (2014). The use of acoustic indices to determine avian species richness in audio-recordings of the environment. Ecological Informatics, 21, 110–119. https://doi.org/10.1016/j.ecoinf.2013.11.007

See Also

ACIspec() to calculate the Acoustic Complexity Index and bgNoise() to calculate Background Noise and Soundscape Power.

Examples

### This is an 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 ENTspec function with all the default arguments
ent = ENTspec(recDir)

# Here's the result
ent

# Plot ENT values
plot(ent)

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_{m,f} = \begin{cases} 1, & \text{if } BGN_{m,f} > \theta_1 \ \text{ or } POW_{m,f} > \theta_2 \\ 0, & \text{otherwise} \end{cases}

where \theta is a user-defined threshold applied uniformly to both BGN and POW. 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

See Also

multActivity() to run this over multiple audio files and singleSat() to get the full saturation values this activity matrix is derived from.

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 estimates the dominant baseline level of acoustic energy within a frequency window and time bin. It was described by Towsey (2017) based on the approach of Lamel et al. (1981).

For each frequency window f and time bin c, BGN is defined as the modal value of the intensity distribution (in dB), representing the most frequently occurring sound level:

BGN_f = \mathrm{mode}(dB_{c,f})

This value approximates the continuous background component of the soundscape, filtering out transient acoustic events such as bird calls or other short-duration signals.

Soundscape Power (POW) quantifies the contrast between this baseline level and the strongest acoustic events within the same frequency window and time bin. It is defined as:

POW_f = \max(dB_{c,f}) - BGN_f

where \max(dB_{c,f}) is the maximum intensity observed. POW can be interpreted as a proxy for signal-to-noise ratio, with higher values indicating stronger or more prominent acoustic events relative to the background level.

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

See Also

ACIspec() to calculate the Acoustic Complexity Index and ENTspec() to calculate Spectral Entropy from a single audio file. Also, check activity() and singleSat(), which use this same Background Noise and Soundscape Power calculation to determine acoustic activity and saturation.

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_{m,f} = \begin{cases} 1, & \text{if } BGN_{m,f} > \theta_1 \ \text{ or } POW_{m,f} > \theta_2 \\ 0, & \text{otherwise} \end{cases}

where \theta is a user-defined threshold applied uniformly to both BGN and POW. We set 1 to active and 0 to inactive frequency windows.

If backup is set to a valid directory, a file named "SATBACKUP.rds" is automatically saved after every batch of five processed files. This file stores the current processing state and allows interrupted runs (e.g., due to manual termination, session crashes, or system shutdowns) to be resumed using satBackup().

To resume processing, pass the saved file (e.g., "path/SATBACKUP.rds") to satBackup(). Once a backup has been created, all original arguments and file paths must remain unchanged, unless they are explicitly modified within the saved .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

activity() to run this on a single audio file, soundSat() and soundMat() to get saturation values instead. 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 = NULL,
  nbreaks = 5,
  yunit = c("hz", "khz"),
  main = NULL,
  xlab = NULL,
  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.

Plot noise.matrix objects

Description

Plot noise.matrix objects

Usage

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

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.

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.rds", 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) quantifies the proportion of frequency bins that are acoustically active within a given time bin. It wasproposed by Burivalova et al. (2018) as a metric to evaluate the acoustic niche hypothesis.

For each time bin m, an activity matrix a_{m,f} is first constructed across frequency bins f. A frequency bin is considered active if either its background level (BGN) or its soundscape power (POW) exceeds a defined threshold:

a_{m,f} = \begin{cases} 1, & \text{if } BGN_{m,f} > \theta_1 \ \text{ or } POW_{m,f} > \theta_2 \\ 0, & \text{otherwise} \end{cases}

where \theta is a user-defined threshold applied uniformly to both BGN and POW.

Soundscape saturation for time bin m is then calculated as the proportion of active frequency bins:

S_m = \frac{\sum_{f = 1}^{N} a_{m,f}}{N}

where N is the total number of frequency bins. Higher values of SAT indicate a greater fraction of the frequency spectrum being occupied by acoustic activity.

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 saturation 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.

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) quantifies the proportion of frequency bins that are acoustically active within a given time bin. It wasproposed by Burivalova et al. (2018) as a metric to evaluate the acoustic niche hypothesis.

For each time bin m, an activity matrix a_{m,f} is first constructed across frequency bins f. A frequency bin is considered active if either its background level (BGN) or its soundscape power (POW) exceeds a defined threshold:

a_{m,f} = \begin{cases} 1, & \text{if } BGN_{m,f} > \theta_1 \ \text{ or } POW_{m,f} > \theta_2 \\ 0, & \text{otherwise} \end{cases}

where \theta is a user-defined threshold applied uniformly to both BGN and POW.

Soundscape saturation for time bin m is then calculated as the proportion of active frequency bins:

S_m = \frac{\sum_{f = 1}^{N} a_{m,f}}{N}

where N is the total number of frequency bins. Higher values of SAT indicate a greater fraction of the frequency spectrum being occupied by acoustic activity.

After computing S_m, the function evaluates all tested threshold values and selects the one that yields the most normally distributed set of saturation values across time bins. Normality is assessed to identify a threshold that best stabilizes the distribution of SAT.

If backup is set to a valid directory, a file named "SATBACKUP.RData" is automatically saved after every batch of five processed files. This file stores the current processing state and allows interrupted runs (e.g., due to manual termination, session crashes, or system shutdowns) to be resumed using satBackup().

To resume processing, pass the saved file (e.g., "path/SATBACKUP.RData") to satBackup(). Once a backup has been created, all original arguments and file paths must remain unchanged, unless they are explicitly modified within the saved .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 statistics values of the normality test that yielded the most normal distribution. The fourth object (values) contains a data.frame with 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)