Example code for reading BirdNET output and exporting a stratified sample of detections in species subfolders. Here stratification is done by site, week and species. The method could be extended to include sampling by confidence score, month, time of day or any other categories as required.
#devtools::install_github('BritishTrustForOrnithology/AcousticTools')
library(AcousticTools)
#collate some BirdNET outputs
df <- AcousticTools::read_birdnet_results(folder = "H:/Leiothrix Recordings")## Folder contains individual Audacity files
#this location has too many for a simple example so I'm just going to limit it to 3 locations and 3 species for simplicity
df <- subset(df, grepl('ST403|ST7747|ST7847', df$original_wav))
df <- subset(df, birdnet_english_name %in% c('Barn Owl', 'Black-crowned Night-Heron', 'Brown Creeper'))
head(df)## original_wav start
## 31 H:/Leiothrix Recordings/ST4037/20240716-140000-ST4037-BTO-XM.wav 132
## 2115 H:/Leiothrix Recordings/ST4037/20240716-211902-ST4037-BTO-XM.wav 1377
## 2126 H:/Leiothrix Recordings/ST4037/20240716-211902-ST4037-BTO-XM.wav 1422
## 2133 H:/Leiothrix Recordings/ST4037/20240716-211902-ST4037-BTO-XM.wav 1434
## 2350 H:/Leiothrix Recordings/ST4037/20240716-211902-ST4037-BTO-XM.wav 2088
## 2875 H:/Leiothrix Recordings/ST4037/20240717-041600-ST4037-BTO-XM.wav 585
## end birdnet_scientific_name birdnet_english_name score lat lon week
## 31 135 Certhia americana Brown Creeper 0.1393 NA NA NA
## 2115 1380 Tyto alba Barn Owl 0.2209 NA NA NA
## 2126 1425 Tyto alba Barn Owl 0.7851 NA NA NA
## 2133 1437 Nycticorax nycticorax Black-crowned Night-Heron 0.1190 NA NA NA
## 2350 2091 Tyto alba Barn Owl 0.1176 NA NA NA
## 2875 588 Nycticorax nycticorax Black-crowned Night-Heron 0.8613 NA NA NA
## overlap sensitivity min_conf species_list model
## 31 NA NA NA NA NA
## 2115 NA NA NA NA NA
## 2126 NA NA NA NA NA
## 2133 NA NA NA NA NA
## 2350 NA NA NA NA NA
## 2875 NA NA NA NA NA
Currently we have all detections. In this example I’m going to do some stratified sampling by site and week. First, split the file names into component parts. This assumes files are in recognised YYYYMMDD-HHMMSS-Location-Recordist-Microphone.wav format. First split the filename using stringr::str_split_fixed.
library(stringr)## Warning: package 'stringr' was built under R version 4.2.3
#split the original filename parts
bits <- setNames(as.data.frame(stringr::str_split_fixed(string = basename(df$original_wav), pattern = "-|\\.", n = Inf)),
c('date_str', 'time_str', 'loc', 'rec', 'mic','ext'))
head(bits)## date_str time_str loc rec mic ext
## 1 20240716 140000 ST4037 BTO XM wav
## 2 20240716 211902 ST4037 BTO XM wav
## 3 20240716 211902 ST4037 BTO XM wav
## 4 20240716 211902 ST4037 BTO XM wav
## 5 20240716 211902 ST4037 BTO XM wav
## 6 20240717 041600 ST4037 BTO XM wav
#join to the main df
df <- cbind(df, bits)As before, I recommend extracting the date and time as a datetime variable. This allows for making the detection times, but also means we can use the date objects to do temporal sampling, e.g. here making a week number variable for later stratification
Then the detection offset (start) can be added to get the start datetime of the detection. Now convert this to a date using lubridate and add the start time offset for the detection.
library(lubridate)## Warning: package 'lubridate' was built under R version 4.2.3
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
#extract the datetime of the original recording
df$recording_start_dt <- as.POSIXct(paste(df$date_str, df$time_str), format = "%Y%m%d %H%M%S")
#add seconds offset to get detecion datetime
df$detection_dt <- df$recording_start_dt + df$start
#make a week number variable
df$weeknum <- lubridate::week(df$detection_dt)
head(df[,c("original_wav", "start", "recording_start_dt", "detection_dt", "weeknum")])## original_wav start
## 31 H:/Leiothrix Recordings/ST4037/20240716-140000-ST4037-BTO-XM.wav 132
## 2115 H:/Leiothrix Recordings/ST4037/20240716-211902-ST4037-BTO-XM.wav 1377
## 2126 H:/Leiothrix Recordings/ST4037/20240716-211902-ST4037-BTO-XM.wav 1422
## 2133 H:/Leiothrix Recordings/ST4037/20240716-211902-ST4037-BTO-XM.wav 1434
## 2350 H:/Leiothrix Recordings/ST4037/20240716-211902-ST4037-BTO-XM.wav 2088
## 2875 H:/Leiothrix Recordings/ST4037/20240717-041600-ST4037-BTO-XM.wav 585
## recording_start_dt detection_dt weeknum
## 31 2024-07-16 14:00:00 2024-07-16 14:02:12 29
## 2115 2024-07-16 21:19:02 2024-07-16 21:41:59 29
## 2126 2024-07-16 21:19:02 2024-07-16 21:42:44 29
## 2133 2024-07-16 21:19:02 2024-07-16 21:42:56 29
## 2350 2024-07-16 21:19:02 2024-07-16 21:53:50 29
## 2875 2024-07-17 04:16:00 2024-07-17 04:25:45 29
Now do the stratified sampling. This can be done in nested loops but this gets messy the more strata there are. So I’ve tried to make this more easily scalable by creating a list (strata) that contains the unique permutations of location, week and species that we have in the data.
#decide how many chunks per stratum
nchunks <- 5
#strata variables
strata_vars <- c('loc', 'weeknum', 'birdnet_english_name')
#get the unique permutations of these - essentially the folders we will need
#which columns contain these?
strata_col_indx <- which(names(df) %in% strata_vars)
#use col indices to get these columns and get unique set
strata <- unique(df[,strata_col_indx])
head(strata)## birdnet_english_name loc weeknum
## 31 Brown Creeper ST4037 29
## 2115 Barn Owl ST4037 29
## 2133 Black-crowned Night-Heron ST4037 29
## 51450 Black-crowned Night-Heron ST4037 30
## 53493 Brown Creeper ST4037 30
## 65266 Brown Creeper ST7747 23
Now we can use this to do the sampling for each stratum. In this example there are two methods for sampling: ‘random’ sampling or ‘tophits’, where the latter just gives the N highest scoring detections. Ultimately, I can pass the output locations to the extract_chunk function which will automatically make any required folders.
#how to select chunks
method <- 'tophits'
#method <- 'random'
#iterate over strata
#make a list to hold outputs
strata_out <- list()
for(i in 1:nrow(strata)) {
#get detections for this stratum
this_stratum <- strata[i,]
#this line will need modifying to match whatever strata vars the filtering is being done on
temp <- subset(df, loc == this_stratum$loc & weeknum == this_stratum$weeknum & birdnet_english_name == this_stratum$birdnet_english_name)
#if number of detections <= nchunks, keep all
if(nrow(temp) <= nchunks) {
stratum_out <- temp
}
#if number of detections > nchunks, use 'method' to select nchunks
if(nrow(temp) > nchunks) {
#random sample
if(method == 'random') {
stratum_out <- temp[sample(nrow(temp), nchunks), ]
}
#top scoring
if(method == 'tophits') {
temp <- temp[order(-temp$score),]
stratum_out <- temp[1:nchunks,]
}
}
#store output for this stratum
strata_out[[i]] <- stratum_out
#tidy up
rm(list=c('stratum_out', 'this_stratum', 'temp'))
} #end stratum loop
#unpack all stratum outputs to a flat df
df_sampled <- do.call(rbind, strata_out)Now we can use the detection datetime to make a unique filename. And we can use the locs and species names to make folders
#make the new filename. Use __ to infer unknown species at this point
df_sampled$newfilename <- paste0(format(df_sampled$detection_dt, "%Y%m%d-%H%M%S"),
'-',
df_sampled$loc,
'-',
df_sampled$rec,
'-',
df_sampled$mic,
'-__.',
df_sampled$ext)
#where will chunks be saved?
path_export <- 'C:/exports'
#make the full path and filename of each clip to be exported
#this line will need to modified for different strata, to make a folder level for each stratum variable
#here I've done site > species > week, but could be in different orders
df_sampled$chunk_fullname <- file.path(path_export, df_sampled$loc, df_sampled$birdnet_english_name, df_sampled$weeknum, df_sampled$newfilename)
head(df_sampled$chunk_fullname)## [1] "C:/exports/ST4037/Brown Creeper/29/20240717-045236-ST4037-BTO-XM-__.wav"
## [2] "C:/exports/ST4037/Brown Creeper/29/20240720-060220-ST4037-BTO-XM-__.wav"
## [3] "C:/exports/ST4037/Brown Creeper/29/20240721-063514-ST4037-BTO-XM-__.wav"
## [4] "C:/exports/ST4037/Brown Creeper/29/20240717-130118-ST4037-BTO-XM-__.wav"
## [5] "C:/exports/ST4037/Brown Creeper/29/20240717-081350-ST4037-BTO-XM-__.wav"
## [6] "C:/exports/ST4037/Barn Owl/29/20240716-214244-ST4037-BTO-XM-__.wav"
Now we can do the exporting using AcousticTools::extract_chunk. As we pass in the file_chunk which contains the site/species/weeknum this ensures all the chunks are made in their respective folders. extract_chunks automatically makes any folders as needed.
#iterate over the required sampled chunks
for(i in 1:nrow(df_sampled)) {
AcousticTools::extract_chunk(file_wav = df_sampled$original_wav[i],
file_chunk = df_sampled$chunk_fullname[i],
start = df_sampled$start[i],
end = df_sampled$end[i],
chunk_duration = 5,
verbose = TRUE)
}