ENTspec: Spectral Temporal Entropy Index

In Ruido: Soundscape Spectral Metrics

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

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
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)

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)

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