Data annotation for spectrogram extraction
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 30/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Domain
- audio-video-rtc, data
Research direction
Start by reviewing the waveform files and the existing extraction or annotation workflow, though the issue does not name specific files or entry points. Done means annotating about 1,000 files with start and end points for every pulse, while recording harmonics, echoes, and overlapping chirps across the specified bat species.
Written by the indexing model from the issue text.
Description
We need to be able to extract chirps and pulses from a waveform file, ideally using traditional signal processing techniques to detect and segment each chirp. To do this without any machine learning, we have used qualitative distinctions so far but would rather make data-driven decisions on how to best extract a compressed representation. This effort would require annotating ~30 files for each of the 34 North American bat species (~1,000 files total).
The primary task would be to annotate every pulse and place a start / end point on each detection. Annotators would need to note any harmonics, and echos, or overlapping chirps.
- Dominant language
- Python
- Stars
- 10
- Forks
- 3
- Avg merge
- 5d 13h
- Merged PRs (30d)
- 2
Getting set up
Starts the project's dev container in your browser, under your own GitHub account.
- Ships a Dockerfile or Docker Compose file
- No pull request template
- No contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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