WEEPSCOPE —
CRY CLASSIFIER.
A standalone Arduino device that listens to an infant and names the cry — five Dunstan cry types, on-device, with no phone and no internet. The work became a co-authored research paper.
— THE CONSTRAINT
CLASSIFY A
CRY ON A
MICRO-
CONTROLLER.
NO CLOUD.
Dunstan Baby Language describes five characteristic newborn cries — hunger, discomfort, sleepiness, lower gas, burp — that untrained parents struggle to tell apart. The device captures 2 seconds of audio at 16 kHz from an onboard PDM microphone, extracts MFE (Mel-filterbank energy) features, and runs a 1D-CNN on an Arduino Nano 33 BLE — the result shows on an OLED, no phone required.
No labelled dataset existed, so it was built by hand: 3,615 samples (2+ hours) curated against reference cries. MFE beat MFCC and spectrogram features in testing. The full 1D-CNN + GRU model (88.7%) was too large for the board, so it was compressed to a CNN that fits in 168 KB flash, with 5-cycle majority voting recovering practical accuracy.
- Feature search. MFE vs MFCC vs spectrogram, compared empirically — MFE won on accuracy.
- Fit the board. 1D-CNN + GRU (88.7%) compressed to a CNN-only model to live in 168 KB flash, ~1.2 s inference.
- Majority voting. Five inference cycles per decision to suppress false identifications.