BattyBirdNET - Bat Sound Analyzer
April 6, 2025 · View on GitHub
Purpose
The purpose is to assist in the automated classification of bat calls. There are some excellent classifiers out there. However, most are for other animal groups, notably birds, or are limited in their use to researchers. The aim here is to provide a solid classifier for as many regions of the planet as possible that can be built into inexpensive, open systems. It builds upon the work of the BirdNET open source product family that has a similar aim for birds.
With this software you can
- Efficiently process a large number of recordings to detect sequences with bat calls, obtained e.g. from recording devices (audiomoth, any other).
- Classifiers included for European and North American species.
- You can generate files 'cut' to contain the detected calls, get a spectrogram visualization and a tentative identification.
- Meta-data in GUANO format is added to the segmented files that can be then cross-validate with other software systems to corroborate results.
- Train your own bat or other animal detection in normal or ultrasound using your own data or the one provided openly here as training sets.
- Set up a streaming internet API service that returns identifications of bats
- Use a graphical UI to do analysis of files on your PC (Linux, Windows) if you prefer that to the commandline.
The BirdNET systems are using a sampling frequency that is too low to capture bat echo location calls. So here I use the system at significantly higher sampling rates of 256kHz-394kHz or higher and cross train a classifier on the BirdNET artificial neural networks to identify bats. Think of a bat as a bird singing three times as fast.
Used by the automated, real-time bat on device detector BattyBirdNET-Pi (https://github.com/rdz-oss/BattyBirdNET-Pi), an inexpensive real-time monitoring system for bats, birds as well as your own trained classifiers (frogs, or whatever you like).
Key words: bat identification, bat detector, BirdNET-Analyzer, DNN, machine learning, transfer learning, biodiverity , monitoring
Two types of Classifiers
BattyBirdNET-Analyzer comes with two types of classifiers:
- Classifiers to be run 24/7 that ignore noise (crickets, rain, electrical noise, ..) and detect bat calls and identify species as well.
- Classifiers that work on files in which it is already clear that there are bat calls contained, yet not known which species it/they are. Available for Bavaria and USA-EAST classifiers. Use the '--no_noise on' option when using bat_ident.py.
There is a tradeoff in accuracy of species identification and being unaffected by environmental noise. So you need to chose according to your purpose! If you want to run a system 24/7 to detetct bats in all sorts of background noise use the first type. Use the more accurate ones if you already have files in which you know a bat is singing for best results.
License
Enjoy! Feel free to use BattyBirdNET for your acoustic analyses and research. If you do, please cite as:
@misc{Zinck2023,
author = {Zinck, R.D.},
title = {BattyBirdNET - Bat Sound Analyzer},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/rdz-oss/BattyBirdNET-Analyzer }}
}
LICENSE: http://creativecommons.org/licenses/by-nc-sa/4.0/
Also consider the references at the end of the page.
Which species are covered ?
| North America | Europe & UK (collecting data) |
|---|---|
| Antrozous pallidus - Pallid bat | Barbastella barbastellus - Western barbastelle |
| Corynorhinus rafinesquii - Rafinesque's big-eared bat | Eptesicus nilssonii - Northern bat |
| Corynorhinus townsendii - Townsend's big-eared bat | Eptesicus serotinus - Serotine bat |
| Eptesicus fuscus - Big brown bat | Hypsugo savii - Savis pipistrelle |
| Euderma maculatum - Spotted bat | Miniopterus schreibersii - Common bent-wing bat |
| Eumops floridanus - Florida bonneted | Myotis alcathoe - Alcathoe bat |
| Eumops perotis - Greater mastiff bat | Myotis bechsteinii - Bechsteins bat |
| Idionycteris phyllotis - Allen's big-eared bat | Myotis brandtii - Brandt bat |
| Lasionycteris noctivagans - Silver-haired bat | ( Myotis capaccinii - Long-fingered bat ) |
| Lasiurus blossevillii - Western red bat | Myotis dasycneme - Pond bat |
| Lasiurus borealis - Eastern red bat | Myotis daubentonii - Daubentons bat |
| Lasiurus cinereus - Hoary bat | Myotis emarginatus - Geoffroys bat |
| Lasiurus intermedius - Northern yellow bat | Myotis myotis - Greater mouse-eared bat |
| Lasiurus seminolus - Seminole bat | Myotis mystacinus - Whiskered bat |
| Lasiurus xanthinus - Western yellow bat | Myotis nattereri - Natterers bat |
| Myotis austroriparius - Southeastern myotis | ( Nyctalus lasiopterus - Greater noctule bat ) |
| Myotis californicus - California bat | Nyctalus leisleri - Lesser noctule |
| Myotis ciliolabrum - Western small-footed myotis | Nyctalus noctula - Common noctule |
| Myotis evotis - Western long-eared bat | Pipistrellus kuhlii - Kuhls pipistrelle |
| Myotis grisescens - Gray bat | ( Pipistrellus maderensis - Madeira pipistrelle ) |
| Myotis leibii - Eastern small-footed bat | Pipistrellus nathusii - Nathusius pipistrelle |
| Myotis lucifugus - Little brown bat | Pipistrellus pipistrellus - Common pipistrelle |
| Myotis septentrionalis - Northern long-eared bat | Pipistrellus pygmaeus - Soprano pipistrelle |
| Myotis sodalis - Indiana bat | Plecotus spec. - Long eared bat |
| Myotis thysanodes - Fringed bat | ( Rhinolophus blasii - Blasius horseshoe bat ) |
| Myotis velifer - Cave bat | Rhinolophus ferrumequinum - Greater horseshoe bat |
| Myotis volans - Long-legged bat | Rhinolophus hipposideros - Lesser horseshoe bat |
| Myotis yumanensis - Yuma bat | ( Tadarida teniotis - European free-tailed bat ) |
| Nycticeius humeralis - Evening bat | Vespertilio murinus - Parti-coloured bat |
| Nyctinomops femorosaccus - Pocketed free-tailed bat | |
| Nyctinomops macrotis - Big free-tailed bat | |
| Parastrellus hesperus - Canyon bat | |
| Perimyotis subflavus - Tricolored bat | |
| Tadarida brasiliensis - Brazilian free-tailed bat |
Methods and data
The detection works by transfer learning from the bird detection network BirdNET v2.4. This network operates on 48000Hz and 3 second sampling intervals. This will not work for bats as their calls go much higher in frequency rates. Since the network is designed to identify bird calls it is still usefull for cross training for bat calls once the frequency range is adjusted.
The data for North American bats comes from the NABAT machine learning data set. The data for Europe was assembled from the Animal Sound Archive Berlin, the ChiroVox database and the xeno-canto database. It contains echolocation as well as social and distress calls in different quantities for each species.
The cross training / transfer learning itself uses the framework provided by BirdNET-Analyzer originally intended for fine tuneing the model to local bird calls or other wildlife in the human audible range. Essentially, it puts a thin layer of a linear classifier on the output of the BirdNET artificial neural network. Appears to also work for bats if sampling frequencies are adjusted. To cross train your own classifier on top of BirdNET set the config parameters in config.py and run e.g.
python3 train.py --i ../path/to/data --o ./path/to/file-name-you-want.tflite
This expects that your training data is contained in subfolders that have the following name structure 'Lantin name_Common name' such as e.g. 'Antrozous pallidus_Pallid bat'. The labels are parsed from the folder names. You can also have folders for 'noise' or 'background'.
Data sets
The Bavaria and USA-EAST training sets (without the noise files) are provided as is here. You can use the them for your own classifiers as long as conditions of http://creativecommons.org/licenses/by-nc-sa/4.0/ are met.
You are welcome to use it accordingly, e.g. for teaching and research,OSS software and systems as well as for other non-commercial purposes. The Bavaria data set also encompasses UK and nordic species. The data is a random selection from data from xeno-canto, chirovox, animal sound library berlin, as well as individuals (R. Zinck (GER), K. Richards (UK)).
If you have data, consider contacting me as the classifiers can only be as good as the data sets they are trained with. Help us reach the goal of at least 100 samples (3 second recordings) per species!
Install
You can follow the same procedure as for the BirdNET-Analyzer see here. Just remember to use this repository.
If you want to generate spectrograms, you need to install 'sox' for your platform and make sure it is in your systems PATH variable.
To use the graphical UI, you will need to upse pip3 to install pywebview and gradio.
Usage
Clients analyze.py and bat_ident.py
To identify bats, load your audio data into the folder named 'put-your-files-here'. The files can be in .wav (recommended), .flac, .mp3, .ogg or .m4a format. The analysis is set to standard settings that should work for most by default. If you are fine with the EU classfier and hava python 3.10 version installed (check with python3 --version), then
python3 bat_ident.py
is all you need to do to run an analysis on all autio files in that directory. Results will appear in a 'put-your-files-here/results' folder in .csv format. The audio files are analyzed and for each second one or two potential species are listed. These are the most likely bat species to made made the calls in that second. You can also use the more verbose analyze.py
python3 analyze.py --i put-your-files-here/ --o put-your-files-here/results --classifier checkpoints/bats/v1.0/BattyBirdNET-Bavaria-144kHz.tflite
There are several options available, most natably the area setting, e.g.
python3 bat_ident.py --area Scotland
that uses a more area specific subset of bats and hence can reduce some classification erros. You can set the above classifiers via the '--area' flag. Further, you can also set analysis related parameters such as
--no_noise Defaults to off. Uses a classfier traine to ignore environmental noise. If you have no noise in the files, use 'on' for higher accuracy in identification.
--min_conf Minimum confidence threshold. Values in [0.01, 0.99]. Defaults to 0.1.
--overlap Overlap of prediction segments. Values in [0.0, 2.9]. Defaults to 0.0.
--rtype Specifies output format. Values in ['table', 'audacity', 'r', 'kaleidoscope', 'csv'].
and hardware related parameters
--threads Number of CPU threads.
--batchsize Number of samples to process at the same time.
you can also provide custom input and output dirctories
--i Path to input file or folder.
--o Path to output file or folder.
A more complex example might look like this
python3 bat_ident.py --location Bavaria --i test_data/Bavaria --o test_data/Bavaria/results --min_conf 0.4 --overlap 0.3
Graphical user interface
Currently single file and batch file analysis are supported with English labels. You need to have pywebview and gradio installed, e.g. by
pip3 install pywebview gradio
To start the GUI, use
python3 bat_gui.py
it opens up a window and alternatively, you can use it from your browser on http://127.0.0.1:7860 . Once you have analysis files, you can use the segmentats tab to extract segments.
Extracting identified segments and plotting spectrograms
Once you ran bat_ident.py and have a result file, you can obtain the segmented audio files sorted by detected species using
python3 segments.py --audio path/to/input/audio --results path/to/input/audio/results --o path/to/input/audio/segments
If you have put your files in the folder 'put-your-files-here', you can use
python3 segments.py
If you use the above folder and know that you want to extract the segements at the time you run the analyis, you can use
python3 bat_ident.py --segment on
and it will generate the segments to 'put-your-files-here/segments'. If you have installed the program 'sox' on your system (or sox.exe, a command line tool) and it is in your shells 'Path' variable, you can segment and generate .png images of the corresponding diagramy by using
python3 bat_ident.py --spectrum on
REST API - Online service
You can start an API service to analyze your bat calls locally or remotely. You set the area and other parameters once when you start the API service that can then answer http requests
python3 server.py --area EU
e.g. using the client.py or from your remote/local application
python3 client.py --i path/to/audio.wav
References
Papers
FROMMOLT, KARL-HEINZ. "The archive of animal sounds at the Humboldt-University of Berlin." Bioacoustics 6.4 (1996): 293-296.
Görföl, Tamás, et al. "ChiroVox: a public library of bat calls." PeerJ 10 (2022): e12445.
Gotthold, B., Khalighifar, A., Straw, B.R., and Reichert, B.E., 2022, Training dataset for NABat Machine Learning V1.0: U.S. Geological Survey data release, https://doi.org/10.5066/P969TX8F.
Kahl, Stefan, et al. "BirdNET: A deep learning solution for avian diversity monitoring." Ecological Informatics 61 (2021): 101236.
Links
https://www.museumfuernaturkunde.berlin/en/science/animal-sound-archive
https://www.sciencebase.gov/catalog/item/627ed4b2d34e3bef0c9a2f30