Example code for reading BirdNET output and exporting detections in species subfolders
#devtools::install_github('BritishTrustForOrnithology/AcousticTools')
library(AcousticTools)
#read a BirdNET output
df <- AcousticTools::read_birdnet_results(folder = "H:/Leiothrix Recordings/ST4037")## Folder contains individual Audacity files
head(df)## original_wav start end
## 1 H:/Leiothrix Recordings/ST4037/20240716-140000-ST4037-BTO-XM.wav 15 18
## 2 H:/Leiothrix Recordings/ST4037/20240716-140000-ST4037-BTO-XM.wav 21 24
## 3 H:/Leiothrix Recordings/ST4037/20240716-140000-ST4037-BTO-XM.wav 24 27
## 4 H:/Leiothrix Recordings/ST4037/20240716-140000-ST4037-BTO-XM.wav 30 33
## 5 H:/Leiothrix Recordings/ST4037/20240716-140000-ST4037-BTO-XM.wav 33 36
## 6 H:/Leiothrix Recordings/ST4037/20240716-140000-ST4037-BTO-XM.wav 33 36
## birdnet_scientific_name birdnet_english_name score lat lon week
## 1 Gun Gun 0.1106 NA NA NA
## 2 Muscicapa striata Spotted Flycatcher 0.1904 NA NA NA
## 3 Muscicapa striata Spotted Flycatcher 0.6818 NA NA NA
## 4 Muscicapa striata Spotted Flycatcher 0.7349 NA NA NA
## 5 Coccothraustes coccothraustes Hawfinch 0.1544 NA NA NA
## 6 Muscicapa striata Spotted Flycatcher 0.1177 NA NA NA
## overlap sensitivity min_conf species_list model
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
Currently we have all detections. We might want to do some filtering or sampling. For this simple example I’m just going to limit to high scoring detections. More complex approaches, like site- or species-based stratified sampling could be adopted.
#keep all high scoring detections
df <- subset(df, score >=0.9)Assuming files are in recognised YYYYMMDD-HHMMSS format I recommend extracting the date and time as a datetime variable. Then the detection offset (start) can be added to get the start datetime of the detection. 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$original_wav), pattern = "-|\\.", n = Inf)),
c('date_str', 'time_str', 'loc', 'rec', 'mic','ext'))
head(bits)## date_str time_str loc rec mic ext
## 1 20240716 140000 ST4037 BTO XM wav
## 2 20240716 150000 ST4037 BTO XM wav
## 3 20240716 160000 ST4037 BTO XM wav
## 4 20240716 160000 ST4037 BTO XM wav
## 5 20240716 170000 ST4037 BTO XM wav
## 6 20240716 201900 ST4037 BTO XM wav
#join to the main df
df <- cbind(df, bits)Now convert this to a date using lubridate and add the start time offset for the detection.
library(lubridate)## Warning: package 'lubridate' was built under R version 4.2.3
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
#extract the datetime of the original recording
df$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$start
head(df[,c("original_wav", "start", "recording_start_dt", "detection_dt")])## original_wav start
## 78 H:/Leiothrix Recordings/ST4037/20240716-140000-ST4037-BTO-XM.wav 486
## 296 H:/Leiothrix Recordings/ST4037/20240716-150000-ST4037-BTO-XM.wav 240
## 500 H:/Leiothrix Recordings/ST4037/20240716-160000-ST4037-BTO-XM.wav 267
## 540 H:/Leiothrix Recordings/ST4037/20240716-160000-ST4037-BTO-XM.wav 360
## 787 H:/Leiothrix Recordings/ST4037/20240716-170000-ST4037-BTO-XM.wav 447
## 1075 H:/Leiothrix Recordings/ST4037/20240716-201900-ST4037-BTO-XM.wav 132
## recording_start_dt detection_dt
## 78 2024-07-16 14:00:00 2024-07-16 14:08:06
## 296 2024-07-16 15:00:00 2024-07-16 15:04:00
## 500 2024-07-16 16:00:00 2024-07-16 16:04:27
## 540 2024-07-16 16:00:00 2024-07-16 16:06:00
## 787 2024-07-16 17:00:00 2024-07-16 17:07:27
## 1075 2024-07-16 20:19:00 2024-07-16 20:21:12
Now we can use the detection datetime to make a unique filename. In this example we will put chunks in species folders.
#make the new filename. Use __ to infer unknown species at this point
df$newfilename <- paste0(format(df$detection_dt, "%Y%m%d-%H%M%S"),
'-',
df$loc,
'-',
df$rec,
'-',
df$mic,
'-__.',
df$ext)
#where will chunks be saved?
path_export <- 'C:/exports'
#make the full path and filename of each clip to be exported
df$chunk_fullname <- file.path(path_export, df$birdnet_english_name, df$newfilename)
head(df$chunk_fullname)## [1] "C:/exports/Redwing/20240716-140806-ST4037-BTO-XM-__.wav"
## [2] "C:/exports/Redwing/20240716-150400-ST4037-BTO-XM-__.wav"
## [3] "C:/exports/Redwing/20240716-160427-ST4037-BTO-XM-__.wav"
## [4] "C:/exports/Redwing/20240716-160600-ST4037-BTO-XM-__.wav"
## [5] "C:/exports/Marsh Tit/20240716-170727-ST4037-BTO-XM-__.wav"
## [6] "C:/exports/Chestnut-backed Chickadee/20240716-202112-ST4037-BTO-XM-__.wav"
Now we can do the exporting using AcousticTools::extract_chunk. In this example I will export 5 second chunks centred on the 3 second BirdNet detections.
#iterate over chunks - just do first 10 for demo.
n <- nrow(df)
n <- 10
for(i in 1:n) {
AcousticTools::extract_chunk(file_wav = df$original_wav[i],
file_chunk = df$chunk_fullname[i],
start = df$start[i],
end = df$end[i],
chunk_duration = 5,
verbose = TRUE)
}If you want to export fully anonymised files this can be done as follows. This date time steps can be ignored in this case. Remember to export the lookup so you know what L4G4Y06Y3YF2TFF.wav actually was!
library(stringi)## Warning: package 'stringi' was built under R version 4.2.2
#how many files need anonymised names
num_files <- nrow(df)
#make random strings of 15 characters and/or numbers
df$randstrings <- stringi::stri_rand_strings(n = num_files, length = 15, pattern = "[A-Z0-9]")
#make into filenames
df$chunk_fullname_anonymised <- file.path(path_export, paste0(df$randstrings, ".wav"))
head(df$chunk_fullname_anonymised)## [1] "C:/exports/5ITCNU7P6WCGX8G.wav" "C:/exports/RFY9ZO9OD85175D.wav"
## [3] "C:/exports/LH23QWOAT4NIQW5.wav" "C:/exports/PG296924EFKNAJO.wav"
## [5] "C:/exports/OIRBXTYH94N5776.wav" "C:/exports/PG18L4TYHMXA55U.wav"
#remember to save the lookup!
save(df, file = file.path(path_export, "detection_to_anonymised_names_lookup.Rdata"))