whoBIRD
Identify bird sounds in real time with this Android version of BirdNET, a Bird sound recognition for more than 6,000 species worldwide.
https://github.com/woheller69/whobird
Category: Biosphere
Sub Category: Bioacoustics and Acoustic Data Analysis
Keywords
android-app artificial-intelligence birding birdnet birdsong
Last synced: about 12 hours ago
JSON representation
Repository metadata
Identify bird sounds in real time with this Android version of BirdNET. Bird sound recognition for more than 6,000 species worldwide.
- Host: GitHub
- URL: https://github.com/woheller69/whobird
- Owner: woheller69
- License: gpl-3.0
- Created: 2024-01-26T09:19:16.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-05-08T06:41:02.000Z (10 days ago)
- Last Synced: 2025-05-16T23:33:44.279Z (1 day ago)
- Topics: android-app, artificial-intelligence, birding, birdnet, birdsong
- Language: Kotlin
- Homepage:
- Size: 93.9 MB
- Stars: 510
- Watchers: 7
- Forks: 26
- Open Issues: 6
- Releases: 32
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
whoBIRD - Identify bird sounds in real time
Introducing whoBIRD, the ultimate birding companion that can recognize birds by their sounds, anywhere in the world!
Powered by the cutting-edge BirdNET project, whoBIRD boasts an extensive database of over 6,000 bird species worldwide.
Using advanced machine learning algorithms, this Android app can accurately identify birds based on their unique vocalizations.
What's more, whoBIRD performs its magic in real time entirely on your device, without requiring an internet connection.
This means you can use it anytime, anywhere – whether you're deep in the forest or at the edge of a remote lake.
Instructions
Getting Started
At first start the app will download the required BirdNET model files.
Once the app is installed simply open it and it will begin listening and analyzing.
Detection Notifications
If a bird is detected, its name will be briefly displayed. For a detailed list of all detections, navigate to the View tab.
There you can also backup, share, or delete the database containing your observations.
Customization Options
Ignore Date and Place: Disable the meta model that checks if a bird can be present at your location at the current time. Useful when analyzing recordings from other locations.
Show Images: When enabled, an image of the detected bird will be downloaded if the detection probability is high.
Audio Source: Select the audio input that works best for your device. Typically, "Unprocessed" is the recommended choice. If using a USB microphone, select "Microphone".
High Pass Filter: Filter out low frequencies to reduce background noise, such as traffic sounds. For example, a 200Hz filter can help minimize low-frequency noise.
Threshold: Set the minimum probability required for a detection to be displayed. Be cautious when lowering the threshold, as it may lead to an increase in false detections.
Weighted Meta Model: In addition to running the meta model for the current week and location, this version computes the meta model predictions for all weeks of the year at the same location.
The final result is a 50/50 weighted average of:
-The prediction for the current week, and
-The maximum prediction across all weeks.
This approach improves detection of migratory birds, especially when they arrive earlier or leave later than the typical migration period.
Save .wav files: Save a .wav file for each detection in Music directory. Recordings in this directory are not deleted by the app. Make sure to clean up on your own. Requires Android 12+.
WARNING: This option may consume a lot of space in storage.
Language On Android 13+ you can set the app language in Android app settings.
Contribute
For translations use https://toolate.othing.xyz/projects/whobird/
License
This work is licensed under GPLv3, © woheller69
- This app is built on the BirdNET framework by @kahst, published under CC BY NC SA 4.0 license
- At first start it downloads the BirdNet TFLite library from whoBird-TFlite, which is published under CC BY NC SA 4.0 license
- Label files from BirdNET are used under GPL 3.0 with permission from the author
- It uses code from Tensorflow examples, published under Apache 2.0 license
- It uses Zip4j (https://github.com/srikanth-lingala/zip4j) which is licensed under Apache License Version 2.0
- It uses iirj (https://github.com/berndporr/iirj) which is licensed under Apache License Version 2.0
OTHER APPS
Owner metadata
- Name:
- Login: woheller69
- Email:
- Kind: user
- Description:
- Website:
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/68678880?u=a53affb43690fe678c3dd5b758a04ee6f0dc830a&v=4
- Repositories: 42
- Last ynced at: 2023-03-04T23:20:29.442Z
- Profile URL: https://github.com/woheller69
GitHub Events
Total
- Create event: 8
- Release event: 7
- Issues event: 41
- Watch event: 198
- Issue comment event: 68
- Push event: 37
- Pull request event: 16
- Fork event: 11
Last Year
- Create event: 8
- Release event: 7
- Issues event: 41
- Watch event: 198
- Issue comment event: 68
- Push event: 37
- Pull request event: 16
- Fork event: 11
Committers metadata
Last synced: about 13 hours ago
Total Commits: 161
Total Committers: 12
Avg Commits per committer: 13.417
Development Distribution Score (DDS): 0.112
Commits in past year: 65
Committers in past year: 8
Avg Commits per committer in past year: 8.125
Development Distribution Score (DDS) in past year: 0.185
| Name | Commits | |
|---|---|---|
| woheller69 | w****r@w****e | 143 |
| Too Late (bot) | t****e@o****z | 4 |
| rehork | 6****k | 2 |
| mrestivill | m****l@g****m | 2 |
| Oğuz Ersen | o****z@e****e | 2 |
| Fabian Zickgraf | f****f@d****m | 2 |
| rucksack.dev | s****l@g****m | 1 |
| mosiser | 6****r | 1 |
| mjanssens | m****l@n****t | 1 |
| Yurt Page | y****e@g****m | 1 |
| PrinceNorris | 8****e | 1 |
| Joedmin | j****n@j****z | 1 |
Committer domains:
- joedmin.cz: 1
- nexigon.net: 1
- dashdos.com: 1
- ersen.moe: 1
- othing.xyz: 1
Issue and Pull Request metadata
Last synced: 1 day ago
Total issues: 106
Total pull requests: 35
Average time to close issues: about 1 month
Average time to close pull requests: 2 days
Total issue authors: 55
Total pull request authors: 15
Average comments per issue: 4.47
Average comments per pull request: 1.86
Merged pull request: 21
Bot issues: 0
Bot pull requests: 0
Past year issues: 43
Past year pull requests: 25
Past year average time to close issues: about 1 month
Past year average time to close pull requests: 3 days
Past year issue authors: 28
Past year pull request authors: 10
Past year average comments per issue: 2.86
Past year average comments per pull request: 1.96
Past year merged pull request: 15
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- stevedee (6)
- Ricat87 (4)
- seniordevel (4)
- DerSaxxe (4)
- jer194 (4)
- HFPasfho (4)
- Joedmin (4)
- woheller69 (4)
- Hund (3)
- soenkegissel (3)
- Norgus (2)
- Mrnofish (2)
- r2x0t (2)
- githwithit (2)
- pandalamp (2)
Top Pull Request Authors
- toolatebot (5)
- DerSaxxe (4)
- mrestivill (4)
- oersen (4)
- rehork (4)
- zell-mbc (2)
- lxwntr (2)
- mjanssens (2)
- RockerLee (2)
- zickgraf (1)
- soenkegissel (1)
- mosiser (1)
- Joedmin (1)
- yurtpage (1)
- temp-byte (1)
Top Issue Labels
- enhancement (8)
- please test (4)
Top Pull Request Labels
Dependencies
- androidx.appcompat:appcompat 1.2.0 implementation
- androidx.constraintlayout:constraintlayout 2.0.1 implementation
- androidx.core:core-ktx 1.3.1 implementation
- androidx.lifecycle:lifecycle-common-java8 2.2.0 implementation
- androidx.recyclerview:recyclerview 1.1.0 implementation
- com.google.android.material:material 1.2.1 implementation
- org.jetbrains.kotlin:kotlin-stdlib 1.6.10 implementation
- org.tensorflow:tensorflow-lite 2.6.0 implementation
- org.tensorflow:tensorflow-lite-select-tf-ops 2.6.0 implementation
- org.tensorflow:tensorflow-lite-support 0.1.0 implementation
Score: 8.731013415269564