Acoustic Complexity Index

Description

Acoustic Complexity Index (ACI) from Pieretti, et al. 2011. The ACI is based on the "observation that many biotic sounds, such as bird songs, are characterized by an intrinsic variability of intensities, while some types of human generated noise (such as car passing or airplane transit) present very constant intensity values" (Pieretti, et al. 2011).

The index was tested to the ACItot calculated using SoundscapeMeter v 1.0.14.05.2012, courtesy of A. Farina.

The results given are accumulative. Very long samples will return very large values for ACI. I recommend to divide by number of minutes to get a range of values easier to compare.

Usage

	acoustic_complexity(soundfile, min_freq = NA, max_freq = NA, j = 5, fft_w = 512)
	

Arguments

Value

Returns a list with three objects per channel

References

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

Examples

	data(tropicalsound)
	ACI <- acoustic_complexity(tropicalsound)
	print(ACI$AciTotAll_left)
	
	summary(ACI)
	

Acoustic Diversity Index

Description

Acoustic Diversity Index from Villanueva-Rivera et al. 2011. The ADI is calculated by dividing the spectrogram into bins (default 10) and taking the proportion of the signals in each bin above a threshold (default -50 dBFS). The ADI is the result of the Shannon index applied to these bins.

Usage

acoustic_diversity(soundfile, max_freq = 10000, db_threshold = -50, 
freq_step = 1000, shannon = TRUE)

Arguments

Value

Returns a list with five objects per channel

Note

The code to calculate the ADI has changed due to an error we detected in the original scripts in which the value was calculated using a different equation. In a test of ~38k files, both ways to calculate were highly correlated. This version of the function uses the Shannon's Diversity Index. To obtain a result using the old calculation, set the argument shannon to FALSE. Please check the vignette "Changes in the Acoustic Diversity Index", included in the package, for more details.

For audio files with one channel, the results are showed as the left channel, the right channel returns NA.

References

Villanueva-Rivera, L. J., B. C. Pijanowski, J. Doucette, and B. Pekin. 2011. A primer of acoustic analysis for landscape ecologists. Landscape Ecology 26: 1233-1246. doi: 10.1007/s10980-011-9636-9.

Examples

	data(tropicalsound)
	result <- acoustic_diversity(tropicalsound)
	
	print(result$adi_left)
	
	summary(result)
	

Acoustic Evenness Index

Description

Acoustic Evenness Index from Villanueva-Rivera et al. 2011 (band evenness using the Gini index). The AEI is calculated by dividing the spectrogram into bins (default 10) and taking the proportion of the signals in each bin above a threshold (default -50 dBFS). The AEI is the result of the Gini index applied to these bins.

Usage

acoustic_evenness(soundfile, max_freq = 10000, db_threshold = -50, freq_step = 1000)

Arguments

Value

Returns a list with five objects per channel

Note

For audio files with one channel, the results are showed as the left channel, the right channel returns NA.

References

Villanueva-Rivera, L. J., B. C. Pijanowski, J. Doucette, and B. Pekin. 2011. A primer of acoustic analysis for landscape ecologists. Landscape Ecology 26: 1233-1246. doi: 10.1007/s10980-011-9636-9.

Examples

	data(tropicalsound)
	result <- acoustic_evenness(tropicalsound)
	print(result$aei_left)
	
	summary(result)
	

Bioacoustic Index

Description

Bioacoustic Index from Boelman, et al. 2007. Inspired on Matlab code courtesy of NT Boelman. Several parts where changed, in particular log math, so this won't be directly comparable to the original code in the paper.

The Bioacoustic Index is calculated as the "area under each curve included all frequency bands associated with the dB value that was greater than the minimum dB value for each curve. The area values are thus a function of both the sound level and the number of frequency bands used by the avifauna" (Boelman, et al. 2007).

Usage

bioacoustic_index(soundfile, min_freq = 2000, max_freq = 8000, fft_w = 512)

Arguments

Value

Returns a list with one object per channel

References

Boelman NT, Asner GP, Hart PJ, Martin RE. 2007. Multi-trophic invasion resistance in Hawaii: bioacoustics, field surveys, and airborne remote sensing. Ecological Applications 17: 2137-2144.

Examples

	data(tropicalsound)
	bioindex <- bioacoustic_index(tropicalsound)
	print(bioindex$left_area)
	
	summary(bioindex)
	

Measure a signal or song in a wavefile

Description

This function lets the user select bounding boxes to get statistics of the signals of interest in a sound file.

Usage

measure_signals(wavfile, wl = 512, min_freq = NA, max_freq = NA, min_time = NA,
  max_time = NA, plot_range = 50, dBFS_range = 30, sample_size = 1,
  resultfile = NA, channel = "left")

Arguments

Value

The function will open a spectrogram plot to allow the user to click on the regions of interest. Once all the samples are selected, the function saves a file with the values measured in each sample. In addition, the results of the function dfreq of the package seewave are saved on a folder named the same as the wavfile, without the .wav extension.

Note

For the dBFS_range argument, the code uses the maximum of the values inside the selected region and saves as a resulting signal the values that fall between (maximum - dBFS_range) and the maximum. A selected region with a maximum value of -5 and dBFS_range set to 30 will consider the area with values between -35 and -5 dBFS as a signal.

The function creates a folder dfreq where it saves csv files with the results of the function dfreq from seewave. The name of each file is coded as: wavfile.samplenumber.csv

Examples

## Not run: 
#Take 5 samples of the file file.wav between 1 - 4 kHz, from 10 to 30 seconds.
measure_signals(wavfile="file.wav", wl=2048, min_freq=1, max_freq=4,
  dBFS_range=30, min_time=10, max_time=30, sample_size=5,
  resultfile="results.csv", plot_range=70)
	
## End(Not run)
	

Multiple sound files

Description

Function to extract the specified index from all the wav or flac files in a directory. The results, including the filename and wave technical details, are saved to a csv file. If the computer has multiple cores, it can run files in parallel.

Usage

multiple_sounds(directory, resultfile, soundindex, no_cores = 1, 
flac = FALSE, from = NA, to = NA, units = NA, ...)

Arguments

Note

FLAC stands for Free Lossless Audio Codec. Files in FLAC format have been compressed without destruction of data, which happens in lossy compression codecs like the popular MP3. Files can be between 40-60% of the size of the original wav file, although this value depends on the contents. For more information and to download FLAC, visit http://xiph.org/flac/

Examples

	## Not run: 
		#Calculate the ACI of all the wav
		# files in the directory "/home/user/wavs/"
		# using the function acoustic_complexity
		multiple_sounds(directory = "/home/user/wavs/", 
			resultfile = "/home/user/results.csv", 
			soundindex = "acoustic_complexity")
		
		#Calculate the same as above using 12000Hz as the
		# maximum frequency instead of the default.
		multiple_sounds(directory = "/home/user/wavs/", 
			resultfile = "/home/user/results.csv", 
			soundindex = "acoustic_complexity", max_freq = 12000)
			
		#Calculate the same as above using two cores
		multiple_sounds(directory = "/home/user/wavs/", 
			resultfile = "/home/user/results.csv", 
			soundindex = "acoustic_complexity", no_cores = 2)
			
		#Calculate the same as above using all the cores
		# the computer has
		multiple_sounds(directory="/home/user/wavs/", 
			resultfile = "/home/user/results.csv", 
			soundindex = "acoustic_complexity", no_cores = "max")
			
		#Calculate the same as above using all but one cores
		multiple_sounds(directory = "/home/user/wavs/", 
			resultfile = "/home/user/results.csv", 
			soundindex = "acoustic_complexity", no_cores = -1)
		
## End(Not run)
	

Normalized Difference Soundscape Index

Description

Normalized Difference Soundscape Index (NDSI) from REAL and Kasten, et al. 2012. The NDSI seeks to "estimate the level of anthropogenic disturbance on the soundscape by computing the ratio of human-generated (anthrophony) to biological (biophony) acoustic components found in field collected sound samples" (Kasten, et al. 2012).

Tested with Matlab code courtesy of S. Gage.

Usage

ndsi(soundfile, fft_w = 1024, anthro_min = 1000, anthro_max = 2000, 
	bio_min = 2000, bio_max = 11000)

Arguments

Details

The bin size is determined as the difference between anthro_max and anthro_min, by default 1000 Hz.

Value

Returns a list with one object per channel

References

Remote Environmental Assessment Laboratory. http://www.real.msu.edu

Kasten, Eric P., Stuart H. Gage, Jordan Fox, and Wooyeong Joo. 2012. The Remote Environmental Assessment Laboratory's Acoustic Library: An Archive for Studying Soundscape Ecology. Ecological Informatics 12: 50-67. doi: 10.1016/j.ecoinf.2012.08.001

Examples

	data(tropicalsound)
	NDSI <- ndsi(tropicalsound)
	print(NDSI$ndsi_left)
	
	summary(NDSI)
	

ASCII raster from sound file

Description

This function creates a raster file in ASCII format from the spectrogram of a soundfile. This file can be opened in ArcGIS or any other GIS software. For more details see the tutorial of Villanueva-Rivera et al. 2011.

Usage

sound_raster(wavfile = NA, wav_directory = NA, max_freq = 10000, no_cores = 1)

Arguments

Value

The function will save a file for each channel, in the same directory where the files are at, with the extension .asc.

Note

To get a raster file for a single file, use the argument wavfile. For many files, use the argument wav_directory. Do not use both at the same time or the function will return an error.

This function was released with the version 1.3 of the tutorial of the primer paper, available at:

http://ltm.agriculture.purdue.edu/soundscapes/primer/

and at the website of the package:

http://ljvillanueva.github.io/soundecology/

References

Villanueva-Rivera, L. J., B. C. Pijanowski, J. Doucette, and B. Pekin. 2011. A primer of acoustic analysis for landscape ecologists. Landscape Ecology 26: 1233-1246. doi: 10.1007/s10980-011-9636-9.

Examples

## Not run: 
sound_raster(wavfile = "file1.wav")

sound_raster(wav_directory = "/home/user/wavdirectory")

sound_raster(wav_directory = "/home/user/wavdirectory", no_cores = 4)
	
## End(Not run)
	

Soundscape Ecology

Description

Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings.

Details

Author(s)

Luis J. Villanueva-Rivera and Bryan C. Pijanowski

tropicalsound sound example

Description

Sample sound of a digital recording of a chorus of tropical frogs.

Usage

data(tropicalsound)

Format

A Wave object.

Details

Duration = 20 sec. Sampling rate = 22050 Hz.

Source

Recording made at a tropical rainforest in Puerto Rico by Luis J. Villanueva-Rivera.

Examples

	data(tropicalsound)
	
	tropicalsound