Acoustic signals are multidimensional traits; they vary complexly in time, frequency, amplitude and combinations of these dimensions. Generally, in biology we want to measure aspects of acoustic signals that vary in response to the factors predicted by our hypotheses. In some cases we even lack predictions for specific acoustic parameters and we need to evaluate the relative similarity between the variants of a signal in a population. These analyses require a diversity of tools for quantifying the multiple dimensions in which we can decompose the signals.

The warbleR package is designed to quantify the acoustic structure of a population of signals using 4 main methods of analysis. 2 of them are absolute measures of the structure:

  • Spectrographic parameters
  • Statistical descriptors of cepstral coefficients

The other 2 provide a relative similarity value between signals:

  • Spectrographic cross-correlation
  • Dynamic time warping

Spectrographic parameters

The spectro_analysis() function measures the following spectrographic parameters related to amplitude distributions in time and frequency, descriptors of the fundamental and dominant frequency contours and descriptors of harmonic content:

 

Time and frequency

  • duration: signal length (in s)

  • meanfreq: medium frequency. Weighted average frequency by amplitude (in kHz)

  • sd: standard deviation of the amplitude weighted frequency

 

Frequency amplitude distribution

  • freq.median: medium frequency. The frequency at which the signal is divided into two frequency intervals of equal energy (in kHz)

  • freq.Q25: first frequency quartile. The frequency at which the signal is divided into two frequency ranges of 25% and 75% energy respectively (in kHz)

  • freq.Q75: third frequency quartile. The frequency at which the signal is divided into two frequency ranges of 75% and 25% energy respectively (in kHz)

  • freq.IQR: interquartile frequency range. Frequency range between ‘freq.Q25’ and ‘freq.Q75’ (in kHz)

  • sp.ent: spectral entropy. Frequency spectrum energy distribution. Pure tone ~ 0; loud ~ 1

  • peakf: peak frequency. Frequency with the highest energy. This parameter can take a considerable amount of time to measure. Only generated if fast = FALSE. It provides a more accurate measurement of the peak frequency than meanpeakf(), but can be more easily affected by background noise

  • meanpeakf: mean peak frequency. Frequency with the highest energy of the medium frequency spectrum (see meanspec()). Typically more consistent than peakf()

 

Distribution of amplitude in time

  • time.median: average time. The time at which the signal is divided into two time intervals of equal energy (in s)

  • time.Q25: first quartile. The time in which the signal is divided into two time intervals of 25% and 75% energy respectively (in s)

  • time Q75: third quartile. The time in which the signal is divided into two time intervals of 75% and 25% energy respectively (in s)

  • time.IQR: interquartile time range. Time range between ‘time.Q25’ and ‘time.Q75’ (in s)

  • skew (skewness): Asymmetry of the amplitude distribution

  • kurt (kurtosis): measure of “peakedness” of the spectrum

  • time.ent: temporary entropy. Energy distribution in the time envelope. Pure tone ~ 0; loud ~ 1

  • entropy: Product of the spectral and temporal entropy: sp.ent * time.ent

  • sfm: spectral flatness. Similar to sp.ent (pure tone ~ 0; loud ~ 1)

 

Fundamental frequency contour descriptors

  • meanfun: average of the fundamental frequency measured through the signal

  • minfun: minimum fundamental frequency measured through the signal

  • maxfun: maximum fundamental frequency measured through the signal

 

Dominant frequency contour descriptors

  • meandom: average of the dominant frequency measured through the signal

  • mindom: minimum dominant frequency measured through the signal

  • maxdom: maximum of the dominant frequency measured through the signal

  • dfrange: dominant frequency range measured through the signal

  • modindx: modulation index. Calculated as the cumulative absolute difference between adjacent measurements of dominant frequencies divided by the dominant frequency range. 1 means that the signals are not modulated

  • startdom: measurement of dominant frequency at the beginning of the signal

  • enddom: dominant frequency measurement at the end of the signal

  • dfslope: pending change in the dominant frequency over time ((enddom-startdom)/duration). The units are kHz/s

 

Harmonic content descriptors

  • hn_freq: average frequency of the upper ‘n’ harmonics (kHz) The number of harmonics is defined with the argument ‘nharmonics’

  • hn_width: average bandwidth of the upper ‘n’ harmonics (kHz) (see analysis). The number of harmonics is defined with the argument ‘nharmonics’

  • harmonics: the amount of energy in higher harmonics. The number of harmonics is defined with the argument ‘nharmonics’

  • HNR: relationship between harmonics and noise (dB). A measure of harmonic content

 

We can easily measure them as follows:

library(warbleR)

# load examples
data("lbh_selec_table")

# global parameters
warbleR_options(wav.path = "./examples", flim = c(1, 10), wl = 200, ovlp = 90, pb = FALSE)

sp <- spectro_analysis(lbh_selec_table)

sp
sound.files selec duration meanfreq sd freq.median freq.Q25 freq.Q75 freq.IQR time.median time.Q25 time.Q75 time.IQR skew kurt sp.ent time.ent entropy sfm meandom mindom maxdom dfrange modindx startdom enddom dfslope meanpeakf
Phae.long1.wav 1 0.1730334 5.979896 1.399059 6.327995 5.293800 6.865314 1.571513 0.0761870 0.0479696 0.1175725 0.0696029 1.999405 7.027830 0.9434264 0.8885049 0.8382390 0.6510692 6.477971 2.30625 8.38125 6.0750 5.518518 7.03125 2.30625 -27.306859 7.14375
Phae.long1.wav 2 0.1630480 5.997299 1.422930 6.212125 5.328746 6.880795 1.552049 0.0763491 0.0452439 0.1149950 0.0697511 1.918356 7.334323 0.9468217 0.8908364 0.8434632 0.6678647 6.712500 3.88125 8.49375 4.6125 4.756098 6.91875 7.25625 2.069942 6.91875
Phae.long1.wav 3 0.1749187 6.018300 1.514853 6.424759 5.150246 6.979144 1.828898 0.0893477 0.0545491 0.1279082 0.0733591 2.496740 11.147728 0.9450838 0.8882080 0.8394311 0.6716602 6.560194 2.30625 8.71875 6.4125 6.842105 2.30625 7.25625 28.298854 7.14375
Phae.long2.wav 1 0.1325709 6.398304 1.340412 6.595971 5.607323 7.380852 1.773529 0.0763038 0.0534126 0.1039639 0.0505512 1.568523 6.016392 0.9424661 0.9000328 0.8482504 0.6086184 6.510728 4.89375 7.93125 3.0375 9.703704 7.14375 6.24375 -6.788820 7.36875
Phae.long2.wav 2 0.1261502 6.308252 1.369242 6.596836 5.605837 7.207292 1.601455 0.0770280 0.0539196 0.0991735 0.0452539 2.470897 10.896039 0.9357725 0.9029598 0.8449650 0.6152336 6.223139 3.09375 7.70625 4.6125 7.048781 5.68125 6.46875 6.242559 6.69375
Phae.long3.wav 1 0.1312195 6.608301 1.092168 6.665328 6.063201 7.343674 1.280473 0.0641852 0.0431095 0.0890929 0.0459835 1.775295 6.632376 0.9301880 0.9007131 0.8378325 0.5700750 6.708750 4.89375 8.04375 3.1500 7.928571 5.68125 7.93125 17.146838 6.69375
Phae.long3.wav 2 0.1301789 6.639859 1.117356 6.674164 6.105325 7.427493 1.322168 0.0689176 0.0449879 0.0938046 0.0488167 1.545851 4.969900 0.9232849 0.9014187 0.8322663 0.5317422 6.532190 4.66875 8.15625 3.4875 7.870968 5.68125 6.58125 6.913561 6.69375
Phae.long3.wav 3 0.1312170 6.580739 1.253000 6.646959 6.029463 7.394054 1.364591 0.0641635 0.0402219 0.0928934 0.0526715 1.802520 5.886959 0.9191879 0.9013920 0.8285486 0.5258369 6.379076 2.98125 8.04375 5.0625 5.244444 2.98125 6.80625 29.150196 6.69375
Phae.long4.wav 1 0.1454249 6.219479 1.478869 6.233074 5.456261 7.305488 1.849227 0.0826911 0.0446722 0.1121557 0.0674835 1.274811 4.458109 0.9643357 0.8959714 0.8640172 0.7599268 6.209416 3.43125 8.71875 5.2875 7.702128 7.93125 3.43125 -30.943804 6.24375
Phae.long4.wav 2 0.1441864 6.462809 1.592876 6.338070 5.630777 7.572366 1.941589 0.0834713 0.0426842 0.1081333 0.0654491 1.695847 6.442755 0.9585943 0.8964128 0.8592962 0.7199148 6.386397 3.31875 9.05625 5.7375 3.921569 8.04375 3.31875 -32.770070 6.24375
Phae.long4.wav 3 0.1450989 6.122156 1.541046 6.081716 5.178639 7.239860 2.061221 0.0806173 0.0436282 0.1100189 0.0663907 1.083042 4.194037 0.9642064 0.8962628 0.8641823 0.7332565 6.180195 3.31875 8.60625 5.2875 6.255319 7.81875 3.31875 -31.013324 6.01875

 

Exercise

 

  • The parameters related to harmonic content were not calculated. How can I have do that?

  • How does measuring harmonic content affect performance?

  • What does the argument ‘threshold’ do?

 

Statistical descriptors of cepstral coefficients

These coefficients were designed to decompose the sounds in a similar way than the human auditory system in order to facilitate speech recognition. The central idea is to compress the acoustic data maintaining only relevant information for the detection of phonetic differences. The principle refers to human hearing using the Mel logarithmic scale whose definition is based on how the human ear perceives frequency and loudness (Sueur 2018). Cepstral coefficients are literally defined as “the result of a cosine transformation of the real logarithm of short-term energy spectra expressed on a Mel frequency scale”.

The descriptive statistics that are extracted from the cepstral coefficients are: minimum, maximum, average, median, asymmetry, kurtosis and variance. It also returns the mean and variance for the first and second derivatives of the coefficients. These parameters are commonly used in the processing and detection of acoustic signals (e.g. Salamon et al 2014). They have been widely used for human voice analysis and its use has extended to mammalian bioacoustics, although they also appear to be useful for quantifying the structure of acoustic signals in other groups.

In warbleR we can calculate statistical descriptors of cepstral coefficients with the mfcc_stats() function:

cc <- mfcc_stats(X = lbh_selec_table)

cc
sound.files selec min.cc1 min.cc2 min.cc3 min.cc4 min.cc5 min.cc6 min.cc7 min.cc8 min.cc9 min.cc10 min.cc11 min.cc12 min.cc13 min.cc14 min.cc15 min.cc16 min.cc17 min.cc18 min.cc19 min.cc20 min.cc21 min.cc22 min.cc23 min.cc24 min.cc25 max.cc1 max.cc2 max.cc3 max.cc4 max.cc5 max.cc6 max.cc7 max.cc8 max.cc9 max.cc10 max.cc11 max.cc12 max.cc13 max.cc14 max.cc15 max.cc16 max.cc17 max.cc18 max.cc19 max.cc20 max.cc21 max.cc22 max.cc23 max.cc24 max.cc25 median.cc1 median.cc2 median.cc3 median.cc4 median.cc5 median.cc6 median.cc7 median.cc8 median.cc9 median.cc10 median.cc11 median.cc12 median.cc13 median.cc14 median.cc15 median.cc16 median.cc17 median.cc18 median.cc19 median.cc20 median.cc21 median.cc22 median.cc23 median.cc24 median.cc25 mean.cc1 mean.cc2 mean.cc3 mean.cc4 mean.cc5 mean.cc6 mean.cc7 mean.cc8 mean.cc9 mean.cc10 mean.cc11 mean.cc12 mean.cc13 mean.cc14 mean.cc15 mean.cc16 mean.cc17 mean.cc18 mean.cc19 mean.cc20 mean.cc21 mean.cc22 mean.cc23 mean.cc24 mean.cc25 var.cc1 var.cc2 var.cc3 var.cc4 var.cc5 var.cc6 var.cc7 var.cc8 var.cc9 var.cc10 var.cc11 var.cc12 var.cc13 var.cc14 var.cc15 var.cc16 var.cc17 var.cc18 var.cc19 var.cc20 var.cc21 var.cc22 var.cc23 var.cc24 var.cc25 skew.cc1 skew.cc2 skew.cc3 skew.cc4 skew.cc5 skew.cc6 skew.cc7 skew.cc8 skew.cc9 skew.cc10 skew.cc11 skew.cc12 skew.cc13 skew.cc14 skew.cc15 skew.cc16 skew.cc17 skew.cc18 skew.cc19 skew.cc20 skew.cc21 skew.cc22 skew.cc23 skew.cc24 skew.cc25 kurt.cc1 kurt.cc2 kurt.cc3 kurt.cc4 kurt.cc5 kurt.cc6 kurt.cc7 kurt.cc8 kurt.cc9 kurt.cc10 kurt.cc11 kurt.cc12 kurt.cc13 kurt.cc14 kurt.cc15 kurt.cc16 kurt.cc17 kurt.cc18 kurt.cc19 kurt.cc20 kurt.cc21 kurt.cc22 kurt.cc23 kurt.cc24 kurt.cc25 mean.d1.cc var.d1.cc mean.d2.cc var.d2.cc
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(Spectrographic) cross correlation

This analysis correlates the amplitude values in the frequency and time space pairwise for all signals in a selection table. The correlation represents a measure of spectrographic similarity of the signals:

xcor <- cross_correlation(X = lbh_selec_table)

xcor
Phae.long1.wav-1 Phae.long1.wav-2 Phae.long1.wav-3 Phae.long2.wav-1 Phae.long2.wav-2 Phae.long3.wav-1 Phae.long3.wav-2 Phae.long3.wav-3 Phae.long4.wav-1 Phae.long4.wav-2 Phae.long4.wav-3
Phae.long1.wav-1 1.0000000 0.6638508 0.6491063 0.1946160 0.2615196 0.3339740 0.2992381 0.3383126 0.1834400 0.1577694 0.2048526
Phae.long1.wav-2 0.6638508 1.0000000 0.7118060 0.2458648 0.2660671 0.3280084 0.2835975 0.3409463 0.0954318 0.0913951 0.1307878
Phae.long1.wav-3 0.6491063 0.7118060 1.0000000 0.2442306 0.3080008 0.3520654 0.3175826 0.3426542 0.1610333 0.1476679 0.1905664
Phae.long2.wav-1 0.1946160 0.2458648 0.2442306 1.0000000 0.5949237 0.5617453 0.5729078 0.5002876 0.2741691 0.2470523 0.2871090
Phae.long2.wav-2 0.2615196 0.2660671 0.3080008 0.5949237 1.0000000 0.5098819 0.5427296 0.5103951 0.2097443 0.1923960 0.2334284
Phae.long3.wav-1 0.3339740 0.3280084 0.3520654 0.5617453 0.5098819 1.0000000 0.7865409 0.7247518 0.1340282 0.1314775 0.1516756
Phae.long3.wav-2 0.2992381 0.2835975 0.3175826 0.5729078 0.5427296 0.7865409 1.0000000 0.7259070 0.1766590 0.1735262 0.1979071
Phae.long3.wav-3 0.3383126 0.3409463 0.3426542 0.5002876 0.5103951 0.7247518 0.7259070 1.0000000 0.1879558 0.1754047 0.2092285
Phae.long4.wav-1 0.1834400 0.0954318 0.1610333 0.2741691 0.2097443 0.1340282 0.1766590 0.1879558 1.0000000 0.5277140 0.8161098
Phae.long4.wav-2 0.1577694 0.0913951 0.1476679 0.2470523 0.1923960 0.1314775 0.1735262 0.1754047 0.5277140 1.0000000 0.5197698
Phae.long4.wav-3 0.2048526 0.1307878 0.1905664 0.2871090 0.2334284 0.1516756 0.1979071 0.2092285 0.8161098 0.5197698 1.0000000

 

Spectrographic cross-correlation is the standard way to calculate signal similarity for amplitude variation in frequency and time

Exercise

 

  • What does the argument type do and how does it affect the performance of the function?

  • What does the pb argument do?

 

Dynamic time warping

In time series analysis, time dynamics distortion (DTW) is one of the algorithms to measure the similarity between two time sequences, which may vary in their ‘speed’. The sequences are nonlinearly ‘warped’ in the temporal dimension to determine a measure of their similarity independent of certain nonlinear variations in the temporal dimension.

viewSpec

 

The freq_DTW() function extracts the dominant frequency values as a time series and then calculates the acoustic dissimilarity using dynamic time warping. The function uses the approx() function to interpolate values between the dominant frequency measurements:

dtwdist <- freq_DTW(lbh_selec_table)
Phae.long1.wav-1 Phae.long1.wav-2 Phae.long1.wav-3 Phae.long2.wav-1 Phae.long2.wav-2 Phae.long3.wav-1 Phae.long3.wav-2 Phae.long3.wav-3 Phae.long4.wav-1 Phae.long4.wav-2 Phae.long4.wav-3
Phae.long1.wav-1 0.000 12.721 27.827 19.294 21.745 21.226 19.470 25.102 26.294 27.153 23.848
Phae.long1.wav-2 12.721 0.000 18.540 15.873 20.208 17.673 18.154 24.426 26.374 27.737 24.845
Phae.long1.wav-3 27.827 18.540 0.000 29.512 29.095 24.948 28.397 28.176 31.741 33.288 31.189
Phae.long2.wav-1 19.294 15.873 29.512 0.000 14.318 13.815 12.770 17.927 20.954 19.681 20.090
Phae.long2.wav-2 21.745 20.208 29.095 14.318 0.000 11.381 8.124 11.016 25.788 23.667 22.506
Phae.long3.wav-1 21.226 17.673 24.948 13.815 11.381 0.000 7.020 9.972 28.448 31.674 26.223
Phae.long3.wav-2 19.470 18.154 28.397 12.770 8.124 7.020 0.000 8.019 24.890 26.065 23.616
Phae.long3.wav-3 25.102 24.426 28.176 17.927 11.016 9.972 8.019 0.000 31.034 32.289 28.790
Phae.long4.wav-1 26.294 26.374 31.741 20.954 25.788 28.448 24.890 31.034 0.000 10.256 3.606
Phae.long4.wav-2 27.153 27.737 33.288 19.681 23.667 31.674 26.065 32.289 10.256 0.000 8.339
Phae.long4.wav-3 23.848 24.845 31.189 20.090 22.506 26.223 23.616 28.790 3.606 8.339 0.000

 

The function returns a matrix with paired dissimilarity values.

If img = TRUE, the function also produces image files with the spectrograms of the signals listed in the input data frame that shows the location of the dominant frequencies.

freq_DTW(lbh_selec_table, img = TRUE, col = "red", pch = 21, line = FALSE)

 

dfdtw

Frequency contours can be calculated independently using the freq_ts() function. These contours can be adjusted manually with the tailor_sels() function.

 

Exercise

 

  • What do the length.out argument infreq_DTW()?

  • Calculate spectrographic cross-correlation for the inquiry calls from these individuals: c("206433", "279470", "279533", "279820"). The extended selection table can be downloaded as follows:

download.file(url = "https://ndownloader.figshare.com/files/21167052", 
 destfile = "iniquiry_calls.RDS")
  • We can use a binary matrix to represent call membership. It has to be a pairwise matrix in which 0 denotes pairs of calls that belong to the same individual and 1 pairs that belong to different individuals. The following function creates this type of matrix:
#function to create group membership binary matrix
bi_mats <- function(X, labels) {
  
  # create empty matrix to store memebership matrix
  mat <- matrix(nrow = ncol(X), ncol = ncol(X))
 
  # add labels to row and col names
  rownames(mat) <- colnames(mat) <- labels
  
  # add 0 if same group and 1 if else 
  out <- lapply(1:(length(labels) - 1), function(i){
  sapply((i + 1):length(labels), function(j) 
    if (labels[i] == labels[j]) 0 else 1)  
    })

  # add to mat
  mat[lower.tri(mat)] <- unlist(out)

  # retunr as distance matrix
  return(as.dist(mat))
  }

The function takes as arguments the cross-correlation similarity matrix (‘X’ argument) and a label vector indicating group membership (‘labels’ argument). Compare dissimilarity from cross-correlation (1 - correlation matrix) with call membership using Mantel test (you can use vegan::mantel())

 

  • Do the same test but this time using cepstral coefficient cross-correlation

  • Do the same test using dynamic time warping distances

 

Additional measures

Signal-to-noise ratio

sig2noise() measures this parameter. The duration of the margin in which to measure the background noise must be provided (mar argument):

snr <- sig2noise(X = lbh_selec_table, mar = 0.06)

snr
sound.files channel selec start end bottom.freq top.freq SNR
Phae.long1.wav 1 1 1.1693549 1.3423884 2.220105 8.604378 21.88086
Phae.long1.wav 1 2 2.1584085 2.3214565 2.169437 8.807053 21.17991
Phae.long1.wav 1 3 0.3433366 0.5182553 2.218294 8.756604 19.79567
Phae.long2.wav 1 1 0.1595983 0.2921692 2.316862 8.822316 23.60318
Phae.long2.wav 1 2 1.4570585 1.5832087 2.284006 8.888027 26.99167
Phae.long3.wav 1 1 0.6265520 0.7577715 3.006834 8.822316 25.80051
Phae.long3.wav 1 2 1.9742132 2.1043921 2.776843 8.888027 26.05994
Phae.long3.wav 1 3 0.1233643 0.2545812 2.316862 9.315153 24.61822
Phae.long4.wav 1 1 1.5168116 1.6622365 2.513997 9.216586 28.15947
Phae.long4.wav 1 2 2.9326920 3.0768784 2.579708 10.235116 29.30194
Phae.long4.wav 1 3 0.1453977 0.2904966 2.579708 9.742279 24.75542

 

Inflections

Inflections in this case are defined as changes in the slope of a frequency contour. They can be used as a measure of frequency modulation. They can be calculated using the inflections() function on previously measured frequency contours:

cntrs <- freq_ts(X = lbh_selec_table)

inflcts <- inflections(cntrs)
sound.files selec inflections
Phae.long1.wav 1 9
Phae.long1.wav 2 10
Phae.long1.wav 3 8
Phae.long2.wav 1 13
Phae.long2.wav 2 9
Phae.long3.wav 1 11
Phae.long3.wav 2 8
Phae.long3.wav 3 10
Phae.long4.wav 1 5
Phae.long4.wav 2 5
Phae.long4.wav 3 5

 

Calculates parameters at higher levels of organization

Vocalizations can be organized above the basic signal units like in long repertoire songs or multi-syllable calls. We can calculate average or extreme values of acoustic parameters of the sub-units for these higher levels of organization using the function song_analysis():

# add a 'song' column
lbh_selec_table$song <- rep(1:4, each = 3)[1:11]

# measure default parameters
song_analysis(X = lbh_selec_table, song_colm = "song", parallel = 1, pb = TRUE)
sound.files selec start end top.freq bottom.freq song num.elms elm.duration freq.range song.duration song.rate gap.duration
Phae.long1.wav 1 0.3433366 2.3214565 8.807053 2.169437 1 3 0.1703334 6.637617 1.9781199 1.6528271 0.7335599
Phae.long2.wav 1 0.1595983 1.5832087 8.888027 2.284006 2 2 0.1293606 6.604022 1.4236104 1.5414731 1.1648893
Phae.long3.wav 1 0.6265520 0.7577715 8.822316 3.006834 2 1 0.1312195 5.815482 0.1312195 NA NA
Phae.long3.wav 1 0.1233643 2.1043921 9.315153 2.316862 3 2 0.1306979 6.998291 1.9810279 1.0805852 1.7196320
Phae.long4.wav 1 1.5168116 1.6622365 9.216586 2.513997 3 1 0.1454249 6.702589 0.1454249 NA NA
Phae.long4.wav 1 0.1453977 3.0768784 10.235116 2.579708 4 2 0.1446427 7.655408 2.9314808 0.7175417 2.6421954

This can also be done on parameters extracted from other functions:

# measure acoustic parameters
sp <- spectro_analysis(lbh_selec_table[1:8, ], bp = c(1, 11), 300, fast = TRUE)

sp <- merge(sp, lbh_selec_table[1:8, ], by = c("sound.files", "selec"))

# caculate song-level parameters for all numeric parameters
song_analysis(X = sp, song_colm = "song", parallel = 1, pb = TRUE)
sound.files selec start end top.freq bottom.freq song duration meanfreq sd freq.median freq.Q25 freq.Q75 freq.IQR time.median time.Q25 time.Q75 time.IQR skew kurt sp.ent time.ent entropy sfm meandom mindom maxdom dfrange modindx startdom enddom dfslope meanpeakf num.elms elm.duration freq.range song.duration song.rate gap.duration
Phae.long1.wav 1 0.3433366 2.3214565 8.807053 2.169437 1 0.1703334 6.214573 1.886934 6.398906 5.279726 7.088539 1.808814 0.0812936 0.0493581 0.1209826 0.0716246 2.641668 11.645191 0.9311848 0.8946313 0.8330688 0.5799687 6.717429 4.2125 8.4875 4.2750 4.743245 7.0625 7.2125 0.8991633 6.958953 3 0.1703334 6.637617 1.9781199 1.652827 0.7335599
Phae.long2.wav 1 0.1595983 1.5832087 8.888027 2.284006 2 0.1293606 6.587160 1.811403 6.713650 5.632792 7.562909 1.930116 0.0777719 0.0538421 0.1016897 0.0478477 2.543173 11.544951 0.9264893 0.9119612 0.8449154 0.5528136 6.397644 4.0125 7.7250 3.7125 5.151639 6.0000 6.4125 3.4570754 7.059628 2 0.1293606 6.604022 1.4236104 1.541473 1.1648893
Phae.long3.wav 1 0.6265520 0.7577715 8.822316 3.006834 2 0.1312195 6.737204 1.649035 6.724085 6.053354 7.585366 1.532012 0.0626394 0.0402682 0.0894848 0.0492167 2.502406 10.411846 0.9105878 0.9112879 0.8298076 0.4934158 6.622331 4.8375 8.0625 3.2250 3.674419 7.0125 8.0625 8.0018575 6.757601 1 0.1312195 5.815482 0.1312195 NA NA
Phae.long3.wav 1 0.1233643 2.1043921 9.315153 2.316862 3 0.1306979 6.713171 1.644077 6.711681 6.046112 7.591542 1.545430 0.0664694 0.0410781 0.0940970 0.0530189 2.280022 8.186695 0.9069198 0.9123649 0.8274417 0.4830629 6.315848 4.6500 7.2750 2.6250 4.211745 5.7000 6.8250 8.5827023 6.719848 2 0.1306979 6.998291 1.9810279 1.080585 1.7196320

 

Calculate song-level parameters selecting parameters with ‘mean_colm’:

# caculate song-level parameters selecting parameters with mean_colm
song_analysis(X = sp, song_colm = "song",mean_colm = c("dfrange", "duration"), parallel = 1, pb = TRUE)
sound.files selec start end top.freq bottom.freq song dfrange duration num.elms elm.duration freq.range song.duration song.rate gap.duration
Phae.long1.wav 1 0.3433366 2.3214565 8.807053 2.169437 1 4.2750 0.1703334 3 0.1703334 6.637617 1.9781199 1.652827 0.7335599
Phae.long2.wav 1 0.1595983 1.5832087 8.888027 2.284006 2 3.7125 0.1293606 2 0.1293606 6.604022 1.4236104 1.541473 1.1648893
Phae.long3.wav 1 0.6265520 0.7577715 8.822316 3.006834 2 3.2250 0.1312195 1 0.1312195 5.815482 0.1312195 NA NA
Phae.long3.wav 1 0.1233643 2.1043921 9.315153 2.316862 3 2.6250 0.1306979 2 0.1306979 6.998291 1.9810279 1.080585 1.7196320

 

Calculate song-level parameters for selecting parameters with ‘mean_colm’, ‘max_colm’ and ‘min_colm’ and weighted by duration:

song_analysis(X = sp, weight = "duration", song_colm = "song",
mean_colm =  c("dfrange", "duration"), min_colm =  "mindom", max_colm = "maxdom", 
  parallel = 1, pb = TRUE)
sound.files selec start end top.freq bottom.freq song dfrange duration min.mindom max.maxdom num.elms elm.duration freq.range song.duration song.rate gap.duration
Phae.long1.wav 1 0.3433366 2.3214565 8.807053 2.169437 1 4.281334 0.1704927 3.8625 8.6625 3 0.1703334 6.637617 1.9781199 1.652827 0.7335599
Phae.long2.wav 1 0.1595983 1.5832087 8.888027 2.284006 2 3.691095 0.1294402 3.0375 7.8375 2 0.1293606 6.604022 1.4236104 1.541473 1.1648893
Phae.long3.wav 1 0.6265520 0.7577715 8.822316 3.006834 2 3.225000 0.1312195 4.8375 8.0625 1 0.1312195 5.815482 0.1312195 NA NA
Phae.long3.wav 1 0.1233643 2.1043921 9.315153 2.316862 3 2.623809 0.1307000 4.6125 7.5375 2 0.1306979 6.998291 1.9810279 1.080585 1.7196320

 

 

Exercise

 

  • Spix’s disc-winged bats (Thyroptera tricolor) its a Neotropical species that uses a specific call type to reply to social mates looking for their roosts. Those ‘response’ calls look like this:

 

viewSpec

 

An extended selection table with response calls can be read from github as follows:

download.file(url = "https://github.com/maRce10/BOKU-Analysis-of-animal-acoustic-signals-in-R-2022/raw/master/examples/response_calls.RDS", 
 destfile = "./examples/response_calls.RDS")

response_calls <- readRDS("./examples/response_calls.RDS")

 

  • Calculate spectrographic parameters (spectro_analysis()) for the Spix’s disc-winged bat response calls.

  • Summarize parameters by call (song_analysis()). To do that you should add the column ‘start’, ‘end’ and ‘call’ to the output of spectro_analysis()

 

References

  1. Araya-Salas M, A Hernández-Pinsón N RojasΔ, G Chaverri. (2020). Ontogeny of an interactive call-and-response system in Spix’s disc-winged bats. Animal Behaviour.

  2. Araya-Salas M, Smith-Vidaurre G (2017) warbleR: An R package to streamline analysis of animal acoustic signals. Methods Ecol Evol 8:184–191.

  3. Lyon, R. H., & Ordubadi, A. (1982). Use of cepstra in acoustical signal analysis. Journal of Mechanical Design, 104(2), 303-306.

  4. Salamon, J., Jacoby, C., & Bello, J. P. (2014). A dataset and taxonomy for urban sound research. In Proceedings of the 22nd ACM international conference on Multimedi. 1041-1044.

Session information

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=es_ES.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=es_CR.UTF-8        LC_COLLATE=es_ES.UTF-8    
##  [5] LC_MONETARY=es_CR.UTF-8    LC_MESSAGES=es_ES.UTF-8   
##  [7] LC_PAPER=es_CR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=es_CR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] kableExtra_1.3.4   warbleR_1.1.27     NatureSounds_1.0.4 knitr_1.37        
## [5] seewave_2.2.0      tuneR_1.3.3.1     
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.8        highr_0.9         bslib_0.2.5.1     compiler_4.1.1   
##  [5] jquerylib_0.1.4   Sim.DiffProc_4.8  shinyBS_0.61      bitops_1.0-7     
##  [9] tools_4.1.1       digest_0.6.29     viridisLite_0.4.0 jsonlite_1.7.2   
## [13] evaluate_0.15     lifecycle_1.0.1   fftw_1.0-6.1      rlang_1.0.2      
## [17] cli_3.2.0         rstudioapi_0.13   yaml_2.3.5        parallel_4.1.1   
## [21] xfun_0.30         fastmap_1.1.0     xml2_1.3.2        stringr_1.4.0    
## [25] httr_1.4.2        systemfonts_1.0.2 sass_0.4.0        webshot_0.5.2    
## [29] svglite_2.0.0     glue_1.6.2        R6_2.5.1          dtw_1.22-3       
## [33] pbapply_1.5-0     soundgen_2.2.0    rmarkdown_2.10    magrittr_2.0.2   
## [37] scales_1.1.1      htmltools_0.5.2   MASS_7.3-54       rvest_1.0.1      
## [41] colorspace_2.0-3  Deriv_4.1.3       stringi_1.7.6     proxy_0.4-26     
## [45] munsell_0.5.0     signal_0.7-7      RCurl_1.98-1.6    rjson_0.2.21