Code
# options to customize chunk outputs
knitr::opts_chunk$set(
tidy.opts = list(width.cutoff = 65),
tidy = TRUE,
message = FALSE
)
# ggplot font size
bs <- 12Vocal variation in thick-billed parrots
Acoustic analysis
Code
# print link to github repo if any
if (file.exists("./.git/config")){
config <- readLines("./.git/config")
url <- grep("url", config, value = TRUE)
url <- gsub("\\turl = |.git$", "", url)
cat("\nSource code and data found at [", url, "](", url, ")", sep = "")
}Source code and data found at https://github.com/maRce10/thick_billed_parrots_vocal_variation
Purpose
- Quantify contribution of different social levels of organization in the geographic variation of thick-billed parrots calls.
Report overview
Analysis flowchart
flowchart LR A(Read data) --> B(Create extended<br>selection tables) B --> C(Measure acoustic<br>features) C --> D(Explore acoustic spaces) style A fill:#44015466 style B fill:#3E4A894D style C fill:#26828E4D style D fill:#6DCD594D
Load packages
Code
# knitr is require for creating html/pdf/word reports formatR is
# used for soft-wrapping code
# install/ load packages
sketchy::load_packages(packages = c("knitr", "formatR", "warbleR",
"ggplot2", "maRce10/PhenotypeSpace"))1 Create extended selection tables
Code
# read laughs
est_laughs <- warbleR::selection_table(path = "./data/raw/audio_files/thick_billed_parrots/laughs/",
whole.recs = TRUE, extended = TRUE)
est_laughs <- resample_est(est_laughs, samp.rate = 22.05)
dat_laughs <- read.csv("./data/raw/audio_files/thick_billed_parrots/laughs/TBP_Laugh1stNote-CallList_20Jul25.csv")
dat_laughs$CallFile <- paste0(gsub("\\.aif", "", dat_laughs$CallFile),
"_1")
merged_laughs <- merge(est_laughs, dat_laughs, by.x = "sound.files",
by.y = "CallFile", all.x = TRUE)
merged_laughs <- fix_extended_selection_table(merged_laughs, est_laughs)
merged_laughs$type <- "laughs"
# read barks
est_barks <- warbleR::selection_table(path = "./data/raw/audio_files/thick_billed_parrots/barks",
whole.recs = TRUE, extended = TRUE)
est_barks <- resample_est(est_barks, samp.rate = 22.05)
dat_barks <- read.csv("./data/raw/audio_files/thick_billed_parrots/barks/TBP_Bark-CallList_20Jul25.csv")
dat_barks <- dat_barks[(dat_barks$Call.File) != "", ]
names(dat_barks) <- names(dat_laughs)
dat_barks$CallFile <- paste0(gsub("\\.aif", ".wav", dat_barks$CallFile),
"_1")
merged_barks <- merge(est_barks, dat_barks, by.x = "sound.files",
by.y = "CallFile", all.x = TRUE)
merged_barks <- fix_extended_selection_table(merged_barks, est_barks)
merged_barks$type <- "barks"
tbp_calls <- rbind(merged_laughs, merged_barks)
saveRDS(tbp_calls, file = "./data/processed/extended_selection_table_tpb_calls.rds")2 Measure acoustic features
Five sets of acoustic features are measured: - Cross-correlation - MFCCs - Spectral features - Spectral + harmonic features - Wavelet features
Code
tbp_calls <- readRDS("./data/processed/extended_selection_table_tpb_calls.rds")
# cross correlation
tbp_xc <- warbleR::cross_correlation(X = tbp_calls, ovlp = 70, bp = c(0.1,
8), parallel = 20)
# save results
saveRDS(tbp_xc, file = "./data/processed/cross_correlation_tpb_calls.rds")
# mfccs
tbp_mfcc <- warbleR::mfcc_stats(X = tbp_calls, ovlp = 70, bp = c(0.1,
8), parallel = 20)
# save results
saveRDS(tbp_mfcc, file = "./data/processed/mfcc_tpb_calls.rds")
# spectral feautures
tbp_spft <- warbleR::spectro_analysis(X = tbp_calls, ovlp = 70, bp = c(0.1,
8), parallel = 20)
# save results
saveRDS(tbp_spft, file = "./data/processed/spectral_features_tpb_calls.rds")
# spectral + harmonic features
tbp_spft_harm <- warbleR::spectro_analysis(X = tbp_calls, ovlp = 70,
bp = c(0.1, 8), parallel = 20, harmonicity = TRUE)
# save results
saveRDS(tbp_spft_harm, file = "./data/processed/spectral_features_harmonics_tpb_calls.rds")
tbp_wpd <- warbleR::wpd_features(X = tbp_calls, parallel = 20)
# save results
saveRDS(tbp_wpd, file = "./data/processed/wavelets_tpb_calls.rds")3 Explore acoustic spaces
3.1 Cross-correlation
Code
tbp_xc <- readRDS("./data/processed/cross_correlation_tpb_calls.rds")
tbp_calls <- readRDS("./data/processed/extended_selection_table_tpb_calls.rds")
tbp_mds <- cmdscale(tbp_xc, k = 2)
tbp_calls$MDS1 <- tbp_mds[, 1]
tbp_calls$MDS2 <- tbp_mds[, 2]
ss_type <- space_similarity(type ~ MDS1 + MDS2, data = as.data.frame(tbp_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(tbp_calls, aes(x = MDS1, y = MDS2, color = type)) + geom_point() +
scale_colour_viridis_d(option = "G", begin = 0.2, end = 0.8, name = "Call type") +
theme_classic(base_size = bs) + labs(title = paste0("Mean density overlap:",
round(ss_type$mean.overlap, 2)), x = "MDS1", y = "MDS2")
Code
ss_type_barks <- space_similarity(Site ~ MDS1 + MDS2, data = as.data.frame(tbp_calls[tbp_calls$type ==
"barks", ]), method = "density.overlap")
ss_type_laughs <- space_similarity(Site ~ MDS1 + MDS2, data = as.data.frame(tbp_calls[tbp_calls$type ==
"laughs", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_barks$mean.overlap, ss_type_laughs$mean.overlap))
ggplot(tbp_calls, aes(x = MDS1, y = MDS2, color = Site)) + geom_point() +
scale_colour_viridis_d(option = "G", begin = 0.2, end = 0.8, name = "Site") +
theme_classic(base_size = bs) + facet_grid(~type, scales = "free") +
labs(title = paste0("Mean density overlap:", round(mean_ovlp,
2)), x = "MDS1", y = "MDS2")
3.2 MFCCs
Code
tbp_mfcc <- readRDS("./data/processed/mfcc_tpb_calls.rds")
# pca
pca <- prcomp(tbp_mfcc[, -c(1:2)], center = TRUE, scale. = TRUE)
tbp_calls$PC1.mfcc <- pca$x[, 1]
tbp_calls$PC2.mfcc <- pca$x[, 2]
ss_type <- space_similarity(type ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(tbp_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(tbp_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = type)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Call type") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
Code
ss_type_barks <- space_similarity(Site ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(tbp_calls[tbp_calls$type ==
"barks", ]), method = "density.overlap")
ss_type_laughs <- space_similarity(Site ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(tbp_calls[tbp_calls$type ==
"laughs", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_barks$mean.overlap, ss_type_laughs$mean.overlap))
ggplot(tbp_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = Site)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Site") + theme_classic(base_size = bs) + facet_grid(~type,
scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
3.3 Spectral features
Code
tbp_spft <- readRDS("./data/processed/spectral_features_tpb_calls.rds")
# pca
pca <- prcomp(tbp_spft[, -c(1:2)], center = TRUE, scale. = TRUE)
tbp_calls$PC1.spft <- pca$x[, 1]
tbp_calls$PC2.spft <- pca$x[, 2]
ss_type <- space_similarity(type ~ PC1.spft + PC2.spft, data = as.data.frame(tbp_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(tbp_calls, aes(x = PC1.spft, y = PC2.spft, color = type)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Call type") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
Code
ss_type_barks <- space_similarity(Site ~ PC1.spft + PC2.spft, data = as.data.frame(tbp_calls[tbp_calls$type ==
"barks", ]), method = "density.overlap")
ss_type_laughs <- space_similarity(Site ~ PC1.spft + PC2.spft, data = as.data.frame(tbp_calls[tbp_calls$type ==
"laughs", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_barks$mean.overlap, ss_type_laughs$mean.overlap))
ggplot(tbp_calls, aes(x = PC1.spft, y = PC2.spft, color = Site)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Site") + theme_classic(base_size = bs) + facet_grid(~type,
scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
3.4 Spectral + harmonic features
Code
tbp_spft_harm <- readRDS("./data/processed/spectral_features_harmonics_tpb_calls.rds")
sub_tbp_calls <- tbp_calls[complete.cases(tbp_spft_harm), ]
# remove NAs
tbp_spft_harm <- tbp_spft_harm[complete.cases(tbp_spft_harm), ]
# pca
pca <- prcomp(tbp_spft_harm[, -c(1:2)], center = TRUE, scale. = TRUE)
sub_tbp_calls$PC1.spftharmft <- pca$x[, 1]
sub_tbp_calls$PC2.spftharmft <- pca$x[, 2]
ss_type <- space_similarity(type ~ PC1.spftharmft + PC2.spftharmft,
data = as.data.frame(sub_tbp_calls), method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(sub_tbp_calls, aes(x = PC1.spftharmft, y = PC2.spftharmft,
color = type)) + geom_point() + scale_colour_viridis_d(option = "G",
begin = 0.2, end = 0.8, name = "Call type") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
Code
ss_type_barks <- space_similarity(Site ~ PC1.spftharmft + PC2.spftharmft,
data = as.data.frame(sub_tbp_calls[sub_tbp_calls$type == "barks",
]), method = "density.overlap")
ss_type_laughs <- space_similarity(Site ~ PC1.spftharmft + PC2.spftharmft,
data = as.data.frame(sub_tbp_calls[sub_tbp_calls$type == "laughs",
]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_barks$mean.overlap, ss_type_laughs$mean.overlap))
ggplot(sub_tbp_calls, aes(x = PC1.spftharmft, y = PC2.spftharmft,
color = Site)) + geom_point() + scale_colour_viridis_d(option = "G",
begin = 0.2, end = 0.8, name = "Site") + theme_classic(base_size = bs) +
facet_grid(~type, scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
3.5 Wavelet features
Code
tbp_wavelet <- readRDS("./data/processed/wavelets_tpb_calls.rds")
# pca
pca <- prcomp(tbp_wavelet[, -c(1:2)], center = TRUE, scale. = TRUE)
tbp_calls$PC1.wpd <- pca$x[, 1]
tbp_calls$PC2.wpd <- pca$x[, 2]
ss_type <- space_similarity(type ~ PC1.wpd + PC2.wpd, data = as.data.frame(tbp_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(tbp_calls, aes(x = PC1.wpd, y = PC2.wpd, color = type)) + geom_point() +
scale_colour_viridis_d(option = "G", begin = 0.2, end = 0.8, name = "Call type") +
theme_classic(base_size = bs) + labs(title = paste0("Mean density overlap:",
round(ss_type$mean.overlap, 2)), x = "PC1", y = "PC2")
Code
ss_type_barks <- space_similarity(Site ~ PC1.wpd + PC2.wpd, data = as.data.frame(tbp_calls[tbp_calls$type ==
"barks", ]), method = "density.overlap")
ss_type_laughs <- space_similarity(Site ~ PC1.wpd + PC2.wpd, data = as.data.frame(tbp_calls[tbp_calls$type ==
"laughs", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_barks$mean.overlap, ss_type_laughs$mean.overlap))
ggplot(tbp_calls, aes(x = PC1.wpd, y = PC2.wpd, color = Site)) + geom_point() +
scale_colour_viridis_d(option = "G", begin = 0.2, end = 0.8, name = "Site") +
theme_classic(base_size = bs) + facet_grid(~type, scales = "free") +
labs(title = paste0("Mean density overlap:", round(mean_ovlp,
2)), x = "PC1", y = "PC2")
Takeaways
- MFCCs seems to do a better job at capturing the acoustic variation in thick-billed parrots calls.
Session information
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.5.0 (2025-04-11)
os Ubuntu 22.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/Costa_Rica
date 2025-07-23
pandoc 3.2 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/x86_64/ (via rmarkdown)
quarto 1.7.31 @ /usr/local/bin/quarto
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
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[3] /usr/lib/R/site-library
[4] /usr/lib/R/library
* ── Packages attached to the search path.
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