Code
# options to customize chunk outputs
knitr::opts_chunk$set(
tidy.opts = list(width.cutoff = 65),
tidy = TRUE,
message = FALSE
)
# ggplot font size
bs <- 12Vocal variation in yellow-naped amazons
Acoustic analysis
Code
# print link to github repo if any
if (file.exists("./.git/config")){
config <- readLines("./.git/config")
url <- grep("url", config, value = TRUE)
url <- gsub("\\turl = |.git$", "", 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 yellow-naped amazons 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 1994
est_1994 <- warbleR::selection_table(path = "./data/raw/audio_files/yellow_naped_amazons/1994",
whole.recs = TRUE, extended = TRUE)
est_1994 <- resample_est(est_1994, samp.rate = 22.05)
dat_1994 <- read.csv("./data/raw/YNA1994_504Calls.csv")
dat_1994$CallFile <- paste0(dat_1994$CallFile, ".wav_1")
merged_1994 <- merge(est_1994, dat_1994, by.x = "sound.files", by.y = "CallFile",
all.x = TRUE)
merged_1994 <- fix_extended_selection_table(merged_1994, est_1994)
merged_1994$type <- "1994"
# read 2005
est_2005 <- warbleR::selection_table(path = "./data/raw/audio_files/yellow_naped_amazons/2005",
whole.recs = TRUE, extended = TRUE)
est_2005 <- resample_est(est_2005, samp.rate = 22.05)
dat_2005 <- read.csv("./data/raw/YNA2005_1173Calls.csv")
dat_2005$CallFile <- paste0(gsub("\\.aif", ".wav", dat_2005$CallFile),
"_1")
merged_2005 <- merge(est_2005, dat_2005, by.x = "sound.files", by.y = "CallFile",
all.x = TRUE)
merged_2005 <- fix_extended_selection_table(merged_2005, est_2005)
merged_2005$Site[grep(merged_2005$sound.files, pattern = "2005_06_03_PEAL_B")] <- "PEAL"
merged_2005$Dialect[grep(merged_2005$sound.files, pattern = "2005_06_03_PEAL_B")] <- "Nor"
merged_2005$Year[grep(merged_2005$sound.files, pattern = "2005_06_03_PEAL_B")] <- 2005
# get individual from name
sf <- merged_2005$sound.files[grep(merged_2005$sound.files, pattern = "2005_06_03_PEAL_B")]
merged_2005$UniqueBird[grep(merged_2005$sound.files, pattern = "2005_06_03_PEAL_B")] <- paste(sapply(strsplit(sf,
"_"), "[[", 4), sapply(strsplit(sf, "_"), "[[", 5), sep = "-")
merged_2005$Site[grep(merged_2005$sound.files, pattern = "2005_06_24_SAPA_B")] <- "SAPA"
merged_2005$Dialect[grep(merged_2005$sound.files, pattern = "2005_06_24_SAPA_B")] <- "Sou"
merged_2005$Year[grep(merged_2005$sound.files, pattern = "2005_06_24_SAPA_B")] <- 2005
# get individual from name
sf <- merged_2005$sound.files[grep(merged_2005$sound.files, pattern = "2005_06_24_SAPA_B")]
merged_2005$UniqueBird[grep(merged_2005$sound.files, pattern = "2005_06_24_SAPA_B")] <- paste(sapply(strsplit(sf,
"_"), "[[", 4), sapply(strsplit(sf, "_"), "[[", 5), sep = "-")
merged_2005$type <- "2005"
merged_1994 <- merged_1994[, names(merged_2005)]
yna_calls <- rbind(merged_1994, merged_2005)
yna_calls$Dialect[yna_calls$Dialect == "Nica"] <- "Nic"
saveRDS(yna_calls, file = "./data/processed/extended_selection_table_yna_calls.rds")2 Measure acoustic features
Five sets of acoustic features are measured: - Cross-correlation - MFCCs - Spectral features - Spectral + harmonic features - Wavelet features
Code
yna_calls <- readRDS("./data/processed/extended_selection_table_yna_calls.rds")
# cross correlation
yna_xc <- warbleR::cross_correlation(X = yna_calls, ovlp = 70, bp = c(0.1,
8), parallel = 20)
# save results
saveRDS(yna_xc, file = "./data/processed/cross_correlation_yna_calls.rds")
# mfccs
yna_mfcc <- warbleR::mfcc_stats(X = yna_calls, ovlp = 70, bp = c(0.1,
8), parallel = 20)
# save results
saveRDS(yna_mfcc, file = "./data/processed/mfcc_yna_calls.rds")
# spectral feautures
yna_spft <- warbleR::spectro_analysis(X = yna_calls, ovlp = 70, bp = c(0.1,
8), parallel = 20)
# save results
saveRDS(yna_spft, file = "./data/processed/spectral_features_yna_calls.rds")
# spectral + harmonic features
yna_spft_harm <- warbleR::spectro_analysis(X = yna_calls, ovlp = 70,
bp = c(0.1, 8), parallel = 20, harmonicity = TRUE)
# save results
saveRDS(yna_spft_harm, file = "./data/processed/spectral_features_harmonics_yna_calls.rds")
yna_wpd <- warbleR::wpd_features(X = yna_calls, parallel = 20)
# save results
saveRDS(yna_wpd, file = "./data/processed/wavelets_yna_calls.rds")3 Explore acoustic spaces
3.1 Cross-correlation
Code
yna_xc <- readRDS("./data/processed/cross_correlation_yna_calls.rds")
yna_calls <- readRDS("./data/processed/extended_selection_table_yna_calls.rds")
yna_mds <- cmdscale(yna_xc, k = 2)
yna_calls$MDS1 <- yna_mds[, 1]
yna_calls$MDS2 <- yna_mds[, 2]
ss_type <- space_similarity(type ~ MDS1 + MDS2, data = as.data.frame(yna_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(yna_calls, aes(x = MDS1, y = MDS2, color = type)) + geom_point() +
scale_colour_viridis_d(option = "G", begin = 0.2, end = 0.8, name = "Period") +
theme_classic(base_size = bs) + labs(title = paste0("Mean density overlap:",
round(ss_type$mean.overlap, 2)), x = "MDS1", y = "MDS2")
Code
ss_type_2005 <- space_similarity(Site ~ MDS1 + MDS2, data = as.data.frame(yna_calls[yna_calls$type ==
"2005", ]), method = "density.overlap")
ss_type_1994 <- space_similarity(Site ~ MDS1 + MDS2, data = as.data.frame(yna_calls[yna_calls$type ==
"1994", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_2005$mean.overlap, ss_type_1994$mean.overlap))
ggplot(yna_calls, aes(x = MDS1, y = MDS2, color = Site)) + geom_point() +
scale_colour_viridis_d(option = "G", begin = 0.2, end = 0.8, name = "Site") +
theme_classic(base_size = bs) + facet_grid(~type, scales = "free") +
labs(title = paste0("Mean density overlap:", round(mean_ovlp,
2)), x = "MDS1", y = "MDS2")
3.2 MFCCs
Code
yna_mfcc <- readRDS("./data/processed/mfcc_yna_calls.rds")
# pca
pca <- prcomp(yna_mfcc[, -c(1:2)], center = TRUE, scale. = TRUE)
yna_calls$PC1.mfcc <- pca$x[, 1]
yna_calls$PC2.mfcc <- pca$x[, 2]
ss_type <- space_similarity(type ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(yna_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(yna_calls, aes(x = PC1.mfcc, y = PC2.mfcc, color = type)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Period") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
Code
ss_type_2005 <- space_similarity(Site ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(yna_calls[yna_calls$type ==
"2005", ]), method = "density.overlap")
ss_type_1994 <- space_similarity(Site ~ PC1.mfcc + PC2.mfcc, data = as.data.frame(yna_calls[yna_calls$type ==
"1994", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_2005$mean.overlap, ss_type_1994$mean.overlap))
ggplot(yna_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
yna_spft <- readRDS("./data/processed/spectral_features_yna_calls.rds")
# pca
pca <- prcomp(yna_spft[, -c(1:2)], center = TRUE, scale. = TRUE)
yna_calls$PC1.spft <- pca$x[, 1]
yna_calls$PC2.spft <- pca$x[, 2]
ss_type <- space_similarity(type ~ PC1.spft + PC2.spft, data = as.data.frame(yna_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(yna_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 = "Period") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
Code
ss_type_2005 <- space_similarity(Site ~ PC1.spft + PC2.spft, data = as.data.frame(yna_calls[yna_calls$type ==
"2005", ]), method = "density.overlap")
ss_type_1994 <- space_similarity(Site ~ PC1.spft + PC2.spft, data = as.data.frame(yna_calls[yna_calls$type ==
"1994", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_2005$mean.overlap, ss_type_1994$mean.overlap))
ggplot(yna_calls, aes(x = PC1.spft, y = PC2.spft, color = Site)) +
geom_point() + scale_colour_viridis_d(option = "G", begin = 0.2,
end = 0.8, name = "Site") + theme_classic(base_size = bs) + facet_grid(~type,
scales = "free") + labs(title = paste0("Mean density overlap:",
round(mean_ovlp, 2)), x = "PC1", y = "PC2")
3.4 Spectral + harmonic features
Code
yna_spft_harm <- readRDS("./data/processed/spectral_features_harmonics_yna_calls.rds")
sub_yna_calls <- yna_calls[complete.cases(yna_spft_harm), ]
# remove NAs
yna_spft_harm <- yna_spft_harm[complete.cases(yna_spft_harm), ]
# pca
pca <- prcomp(yna_spft_harm[, -c(1:2)], center = TRUE, scale. = TRUE)
sub_yna_calls$PC1.spftharmft <- pca$x[, 1]
sub_yna_calls$PC2.spftharmft <- pca$x[, 2]
ss_type <- space_similarity(type ~ PC1.spftharmft + PC2.spftharmft,
data = as.data.frame(sub_yna_calls), method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(sub_yna_calls, aes(x = PC1.spftharmft, y = PC2.spftharmft,
color = type)) + geom_point() + scale_colour_viridis_d(option = "G",
begin = 0.2, end = 0.8, name = "Period") + theme_classic(base_size = bs) +
labs(title = paste0("Mean density overlap:", round(ss_type$mean.overlap,
2)), x = "PC1", y = "PC2")
Code
ss_type_2005 <- space_similarity(Site ~ PC1.spftharmft + PC2.spftharmft,
data = as.data.frame(sub_yna_calls[sub_yna_calls$type == "2005",
]), method = "density.overlap")
ss_type_1994 <- space_similarity(Site ~ PC1.spftharmft + PC2.spftharmft,
data = as.data.frame(sub_yna_calls[sub_yna_calls$type == "1994",
]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_2005$mean.overlap, ss_type_1994$mean.overlap))
ggplot(sub_yna_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
yna_wavelet <- readRDS("./data/processed/wavelets_yna_calls.rds")
# pca
pca <- prcomp(yna_wavelet[, -c(1:2)], center = TRUE, scale. = TRUE)
yna_calls$PC1.wpd <- pca$x[, 1]
yna_calls$PC2.wpd <- pca$x[, 2]
ss_type <- space_similarity(type ~ PC1.wpd + PC2.wpd, data = as.data.frame(yna_calls),
method = "density.overlap")
# graph bidimensional space with gpplot coloring by type
ggplot(yna_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 = "Period") +
theme_classic(base_size = bs) + labs(title = paste0("Mean density overlap:",
round(ss_type$mean.overlap, 2)), x = "PC1", y = "PC2")
Code
ss_type_2005 <- space_similarity(Site ~ PC1.wpd + PC2.wpd, data = as.data.frame(yna_calls[yna_calls$type ==
"2005", ]), method = "density.overlap")
ss_type_1994 <- space_similarity(Site ~ PC1.wpd + PC2.wpd, data = as.data.frame(yna_calls[yna_calls$type ==
"1994", ]), method = "density.overlap")
mean_ovlp <- mean(c(ss_type_2005$mean.overlap, ss_type_1994$mean.overlap))
ggplot(yna_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
- Spectral + harmonic features do a better job at capturing the acoustic variation at the period level
- MFCCs seems to do a better job at capturing the acoustic variation at the individual level
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 2025-07-31
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
abind 1.4-8 2024-09-12 [1] CRAN (R 4.5.0)
bitops 1.0-9 2024-10-03 [3] CRAN (R 4.5.0)
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cachem 1.1.0 2024-05-16 [1] CRAN (R 4.5.0)
class 7.3-23 2025-01-01 [4] CRAN (R 4.4.2)
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cli 3.6.5 2025-04-23 [1] CRAN (R 4.5.0)
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goftest 1.2-3 2021-10-07 [3] CRAN (R 4.1.1)
gridExtra 2.3 2017-09-09 [1] CRAN (R 4.5.0)
gtable 0.3.6 2024-10-25 [1] CRAN (R 4.5.0)
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htmlwidgets 1.6.4 2023-12-06 [1] RSPM (R 4.5.0)
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httr 1.4.7 2023-08-15 [3] CRAN (R 4.5.0)
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magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.5.0)
MASS 7.3-65 2025-02-28 [4] CRAN (R 4.4.3)
Matrix 1.7-3 2025-03-11 [4] CRAN (R 4.4.3)
memoise 2.0.1 2021-11-26 [3] CRAN (R 4.1.2)
mgcv 1.9-3 2025-04-04 [4] CRAN (R 4.5.0)
mime 0.13 2025-03-17 [1] CRAN (R 4.5.0)
miniUI 0.1.2 2025-04-17 [3] CRAN (R 4.5.0)
NatureSounds * 1.0.5 2025-01-17 [1] CRAN (R 4.5.0)
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stringr 1.5.1 2023-11-14 [1] CRAN (R 4.5.0)
tensor 1.5.1 2025-06-17 [1] CRAN (R 4.5.0)
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usethis 3.1.0 2024-11-26 [3] CRAN (R 4.5.0)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.5.0)
vegan 2.7-1 2025-06-05 [1] CRAN (R 4.5.0)
viridis 0.6.5 2024-01-29 [1] CRAN (R 4.5.0)
viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.5.0)
warbleR * 1.1.36 2016-04-19 [1] CRAN (R 4.5.0)
withr 3.0.2 2024-10-28 [1] CRAN (R 4.5.0)
xaringanExtra 0.8.0 2024-05-19 [1] CRAN (R 4.5.0)
xfun 0.52 2025-04-02 [1] CRAN (R 4.5.0)
xtable 1.8-4 2019-04-21 [3] CRAN (R 4.0.1)
yaml 2.3.10 2024-07-26 [1] CRAN (R 4.5.0)
[1] /home/m/R/x86_64-pc-linux-gnu-library/4.5
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library
* ── Packages attached to the search path.
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