Ruido (version 1.0.2)

bgNoise: 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"
)

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.

Arguments

soundfile

tuneR Wave object or path to a valid audio file

channel

channel where the metric values will be extract from. Available channels are: "stereo", "mono", "left" or "right". Defaults to "stereo"

timeBin

size (in seconds) of the time bin. Set to NULL to use the entire audio as a single bin. Defaults to 60

dbThreshold

minimum allowed value of dB for the spectrograms. Defaults to -90, as set by Towsey 2017

targetSampRate

desired sample rate of the audios. This argument is only used to down sample the audio. If NULL, then audio's sample rate remains the same. Defaults to NULL

wl

window length of the spectrogram. Defaults to 512

window

window used to smooth the spectrogram. Switch to signal::hanning(wl) to use hanning instead. Defaults to signal::hammning(wl)

overlap

overlap between the spectrogram windows. Defaults to wl/2 (half the window length)

histbreaks

breaks used to calculate Background Noise. Available breaks are: "FD", "Sturges", "scott" or any numeric value (foe example = 100). Defaults to "FD"

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.

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)")

# \donttest{
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)
# }