bgNoise: Background Noise and Soundscape Power Index

In Ruido: Soundscape Spectral Metrics

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

soundfile	wav package numeric matrix, tuneR package Wave object or path to a .wav file
channel	channel where the metric values will be extracted 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. Set to NULL to leave db values unrestricted 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::hamming(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" and 100. Defaults to "FD". Can also be set to any numerical value to limit or increase the amount of breaks.
DCfix	if the DC offset should be removed before the metrics are calculated. Defaults to TRUE

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")

Ruido documentation built on Sept. 2, 2026, 1:07 a.m.