InsectNet

A BirdNET-Pi sidecar that classifies insect sounds in real time. Research prototype — active development.

What It Is

InsectNet is a lightweight sklearn head trained on BirdNET's 6,522-dim logit space. It runs alongside BirdNET-Pi on a Raspberry Pi, watches the audio stream, and sorts captured WAVs into acoustic classes.

The architecture is simple: StandardScaler → OneVsRest(LogisticRegression). Nothing novel — the interesting part is that BirdNET's logit space encodes insect acoustic structure well enough that a linear probe works for several classes.

What's Validated

Field validation at Pine Hollow, Tennessee (35.8565, -83.3744):

Class	Status	Confidence (field)	Notes
background	Production	N/A	0.984 F1, 1,669 public clips + field negatives
cicada_drone	Working	83-100%	Natural capture at 83%, playback at 99-100%. AC unit false positive at 92%.
frog	Working	51-99%	Natural chorus confirmed. 440+ captures in one evening, two species identified.
cricket_katydid	Likely working	99+%	Playback at 100%. Natural summer data pending.
grasshopper	Data-limited	TBD	183 training clips, 0.701 F1. Not production-ready.
bee	Untrained	TBD	43 training clips, 0.608 F1. No real field captures. Known false positives from weed whacker and night noise.

What It's Not

This is not a finished product. It's a working research prototype that has been field-tested enough to know it catches real insects — and also catches enough false positives to know it shouldn't be trusted blindly.

• The F1 numbers are from cross-validation on public training data, not from field deployment. Actual performance varies with environment, mic placement, and insect proximity.
• All threshold tuning was done over one month at a single location.
• Grasshopper and bee classes need substantially more training data before they can be used without human review.

Known Limitations

• BirdNET dependency. The classifier requires BirdNET's TFLite model to extract logits. Without BirdNET, the classifier can't run.
• Mic placement. The outdoor mic at Pine Hollow is upward-facing for birds. Ground-level insect sounds must be loud to reach it.
• No cicada species channels. BirdNET has zero cicada labels. Cicada detection relies on general acoustic features in the BirdNET embedding space.
• False positives. AC units → cicada_drone (92%). Weed whackers → bee (98%). Night noise → bee (50-70%).
• All BirdNET species IDs are approximate. BirdNET maps to the closest species in its 6,522-label set, which may not be the true species.

How to Use

The classifier alone isn't useful standalone — it needs BirdNET's TFLite model to produce logits. The full capture pipeline lives on GitHub:

https://github.com/vortexpjeff/insectnet

# After extracting BirdNET logits (6,522-dim vector):
import joblib
clf = joblib.load("classifier.joblib")
X = clf["scaler"].transform(logits.reshape(1, -1))
scores = clf["classifier"].predict_proba(X)[0]
for i, cls in enumerate(clf["classes"]):
    print(f"{cls}: {scores[i]*100:.1f}%")

Training Data

Source	Clips	License	Content
InsectSet459	~1,800	CC BY-NC-SA 4.0	459 insect species, primarily Orthoptera
iNatSounds	~1,041	CC BY-NC 4.0	iNaturalist insect observations
ESC-50	1,519	CC BY-NC 4.0	Environmental noise (background class)
Pine Hollow field	38 (unreviewed)	CC BY-NC-SA 4.0	Natural captures from Pi sidecar

All training data and the BirdNET backbone are non-commercial. Derivative classifiers must use a compatible license.

Project Status

Actively developed. Summer 2026 is the primary field data collection window for improving grasshopper, bee, and cricket classes. New captures are being accumulated continuously via the BirdNET-Pi sidecar.

License

CC BY-NC-SA 4.0 — See LICENSE file.