Soundscape Spectral Metrics

Accessible and flexible implementation of spectral ecoacoustic indices. The functions were design to accommodate a variety of sampling designs. Users can tailor calculations by specifying spectrogram time bin size, amplitude thresholds and normality tests. By simplifying computation and standardizing reproducible methods, the package aims to support ecoacoustics studies.

Ruido: An R Package for Calculating Spectral Metrics for Ecoacoustics Research

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Ruido is an R package that aims to provide a simple and accessible framework for calculating spectral soundscape metrics that describes noise dynamics. It provides accessible tools for calculating less common, but ecologically meaningful soundscape metrics, helping researchers move beyond standard and classic indices.

Currently, the package implements methods to estimate:

Background Noise (BGN) and Soundscape Power (POW), following Towsey et al. 2017
Soundscape Saturation (SAT), following Burivalova et al. 2018
Spectral Acoustic Complexity Index (ACI), following Pieretti, et al. 2011
Spectral Temporal Entropy Index (ENT), following Towsei et al. 2017

These metrics can be used to explore acoustic complexity, biotic activity, and environmental disturbance, making Ruido useful for ecological monitoring, bioacoustic surveys, or experimental soundscape studies.

Installation

CRAN Download:

install.packages("Ruido")
library(Ruido)

Github Download:

devtools::install_github("Arthurigorr/Ruido")
library(Ruido)

Examples:

To illustrate the package's use, we are going to use the recordings available at: https://zenodo.org/records/17243660.
Use https://zenodo.org/records/17575795 instead if you want to download lighter files.

If you wish to temporary download the files using R to follow the examples, run:

options(timeout = 500)

dir <- tempdir()
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/17243660/files/",
                rec,
                "?download=1")
  download.file(url, destfile = paste(dir, rec, sep = "/"), mode = "wb")
}

These examples use ggplot2 and patchwork to plot their results. Before running them, first run:

library(ggplot2)
library(patchwork)

Background Noise (BGN) and Soundscape Power (POW)

BGN_POW <- lapply(recDir, bgNoise)

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))
sampRate <- BGN_POW[[1]]@sampRate
kHz <- cumsum(c(0, rep(sampRate / 6, 6))) / 1000
breaks <- round(c(1, cumsum(rep(256 / 6, 6))))

timeLabels <- time[c(1, 7, 13, 19, 24)]
timeBreaks <- as.character(dateTime[c(1, 7, 13, 19, 24)])

plotList <- list()
plotN <- 1

for (ind in c("BGN", "POW")) {
  for (cha in c("left", "right")) {
    core <- do.call(cbind, lapply(BGN_POW, function(x) {
      x@values[[cha]][[ind]]
    }))
    
    sDim <- dim(core)
    
    coreDf <- data.frame(
      TIME = as.character(rep(dateTime, each = sDim[1] * 3) + rep(rep(c(0, 60, 120), each = sDim[1]), sDim[2] / 3)),
      SPEC = rep(seq(sDim[1]), sDim[2]), VAL = c(unlist(core))
    )
    
    plotList[[plotN]] <- ggplot(coreDf, aes(x = TIME, y = SPEC, fill = VAL)) +
      geom_tile() +
      theme_classic() +
      scale_y_continuous(expand = c(0, 0), labels = kHz, breaks = breaks) +
      scale_x_discrete(expand = c(0, 0), labels = timeLabels, breaks = timeBreaks) +
      scale_fill_viridis_c(option = "magma", name = ind) +
      labs( x = "Time of Day", y = "Frequency (kHz)", title = paste(ind, "in the", cha, "channel")
      )
    
    plotN <- plotN + 1
    
  }
}

plotList[[1]] + plotList[[2]] + plotList[[3]] + plotList[[4]]

Soundscape Saturation (SAT)

sat <- soundSat(dir)
SAT <- sat$values

satForPlot <- cbind(
  aggregate(SAT ~ AUDIO + CHANNEL, data = SAT, sd),
  aggregate(SAT ~ AUDIO + CHANNEL, data = SAT, mean)$SAT,
  TIME = rep(substr(time, 1, 5), 2)
)
colnames(satForPlot)[c(3, 4)] <- c("sdSAT", "meanSAT")

ggplot(
  satForPlot,
  aes(x = TIME, y = meanSAT * 100, group = CHANNEL, fill = CHANNEL,
      ymin = pmax(meanSAT - sdSAT, 0) * 100, ymax = pmin(meanSAT + sdSAT, 100) * 100
  )
) +
  geom_ribbon(alpha = 0.5) +
  geom_line() +
  geom_point() +
  theme_classic() +
  scale_y_continuous(limits = c(0, 100), expand = c(0, 0)) +
  scale_x_discrete( expand = c(0, 0), breaks = c("00:00", "06:00", "12:00", "18:00", "23:00")
  ) +
  labs(y = "Soundscape Saturation (%)") +
  theme(
    axis.title.x = element_blank(), axis.text = element_text(size = 15),
    axis.title = element_text(size = 18, face = "bold"),
    legend.title = element_text(size = 15, face = "bold"),
    legend.text = element_text(size = 15)
  ) +
  guides(fill = guide_legend(title = "Side"))

Acoustic Activity

act <- multActivity(dir, powthr = sat$powthresh, bgnthr = sat$bgnthresh / 100)

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))
sampRate <- act$info$SAMPRATE[[1]]
kHz <- cumsum(c(0, rep(sampRate / 6, 6))) / 1000
breaks <- round(c(1, cumsum(rep(256 / 6, 6))))

timeLabels <- time[c(1, 7, 13, 19, 24)]
timeBreaks <- as.character(dateTime[c(1, 7, 13, 19, 24)])

plotList <- list()
plotN <- 1

for (cha in c("left", "right")) {
  actCurrent <- act$values[, act$info$CHANNEL == cha]
  actCurrentDF <- data.frame(
    TIME = as.character(rep(dateTime, each = sDim[1] * 3) + rep(rep(c(0, 60, 120), each = sDim[1]), sDim[2] / 3)),
    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 = timeLabels, breaks = timeBreaks) +
    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(guides = "collect")

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
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
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/