v0.2.0 changes vs v0.1.0: trained at 1:3 pos:neg ratio with per-epoch random negative resampling and a learning-rate drop at epoch 30. Same student architecture, same INT8 size. Best-F1 lifted from 0.696 to 0.756 and FPR at the operating threshold dropped from 31.8 % to 22.7 % — material improvement in deployment-relevant terms. See docs/experiments/EXP-007_per_epoch_resample.md and docs/experiments/EXP-008_lr_schedule.md.

Model description


Architecture	DS-CNN (Conv + 4 depthwise-separable blocks + GAP + Dense-521)
Parameters	80,713
INT8 size	110 KB (`.tflite`)
Teacher	google/yamnet/1 (FP32, 521-class AudioSet)
Distillation loss	KL(teacher_softmax \|\| student_softmax) over the full 521-class space
Input	16 kHz mono PCM → 0.96 s log-mel patch (96 frames × 64 mels), int8
Output	521 raw logits, int8. Cry score = softmax(logits)[19] + softmax(logits)[20]
Quantization	Full-integer TFLite, AudioSet-only calibration (200 patches from `audioset_val.csv`)
Target hardware	ESP32-S3 (Xtensa LX7 dual-core, 8 MB PSRAM); runs equally on TFLite-Micro generic

Performance

Headline: 62-segment AudioSet test slice (frozen at curation, 38 % YouTube takedown attrition since release). 18 cry / 44 negative.

Cry-positive score = student softmax probability mass on YAMNet classes 19 (Crying, sobbing) + 20 (Baby cry, infant cry), averaged over all 0.96 s patches in the 10 s segment.

	best F1	best threshold	precision	recall	AUC	F1 @ 0.5
FP32 student (EXP-008 source)	0.756	0.047	0.630	0.944	0.870	0.286
INT8 quantized (this artifact)	0.744	0.047	—	—	0.866	0.286
Δ from quantization	-0.012	0.000	—	—	-0.004	0.000

INT8 quantization cost was small — 0.012 best-F1 and 0.004 AUC, well under the ≤0.02 AUC budget. The model retains 94 % cry recall at its calibrated threshold.

At the best-F1 threshold (≈0.05), the FP32 model lands 17 of 18 cries caught (94 % recall) with 63 % precision on a balanced 18-pos / 44-neg AudioSet test slice. False-positive rate at this operating point is 22.7 % vs v0.1.0's 31.8 % — a 9-percentage-point reduction (-29 % relative) at the same recall.

Pipeline-evolution context — three methodology shifts that earned this artifact:

EXP-006 — teacher-as-filter (+0.037 AUC over random-window baseline). YAMNet pre-scores every 0.4875 s-hop window of every clip; only confident-positive (p_cry > 0.30) and confident-negative (p_cry < 0.05) windows enter training. See docs/research/methodology-teacher-as-filter.md.
EXP-007 — 1:3 pos:neg ratio + per-epoch negative resampling (+0.060 best-F1, -9 pp FPR at operating threshold). Training at deployment-realistic class ratios pushed the operating point materially without sacrificing AUC.
EXP-008 — LR drop at epoch 30 (+0.009 AUC). Fine-tuning at 10× lower learning rate after the loss plateau closed the train/val gap modestly.

Threshold note: distilled students inherit the teacher's diffuse softmax — cry probabilities concentrate below 0.5 even on obvious cries — so the operating-point threshold (~0.10) is well below the naïve 0.5. Deployments should calibrate per-device with a short on-site recording session.

Training data

Public surface (the headline number stands on this alone):

AudioSet v1 segment IDs committed at data/ids/audioset_*.csv:
- 570 segments train + 160 val + 100 test (frozen)
- Classes: Crying, sobbing (/m/0463cq4), Baby cry, infant cry (/t/dd00002), plus hard-negative classes Speech (/m/09x0r), Babbling (/m/0261r1), Silence (/m/028v0c), Inside, small room (/t/dd00125), Wail, moan (/m/07qw_06), Screaming (/m/03qc9zr)
- Sourced from eval_segments.csv (test) and balanced_train_segments.csv (train+val); reproducible bit-exactly via scripts/curate_audioset_ids.py
- 28.7 % of segments are unrecoverable from YouTube as of distillation; survival rate is recorded in the eval JSONs

Private (augmentation only, never published):

475 in-domain captures from a single Australian household ESP32-S3 deployment, used unsupervised (teacher-logits matching only — no human labels). Captures stay local; nothing capture-derived enters this artifact except via the teacher's intermediate logits during training.
Independent pipeline-trained-without-captures baseline (EXP-003): AUC 0.750, best F1 0.612.
The headline number above (EXP-006) reflects the captures-augmented + teacher-filtered model; the captures-free baseline is ~0.11 AUC weaker.

Calibration set provenance: The INT8 representative dataset (200 patches) was drawn exclusively from audioset_val.csv — never from training data, never from captures, never from the test set. This isolates the published quantization parameters from any capture-derived information.

Intended use

Real-time inference on the ws-ESP32-S3-CAM cry-detect-01 firmware. Pulled into device flash via that repo's tools/fetch_model.sh. Should run equally on any TFLite-Micro target with sufficient flash.

Real-world deployment (verified 2026-05-04)

Confirmed running parallel to the YAMNet teacher on the target ESP32-S3 hardware:

	Teacher (YAMNet INT8)	Student (this model, v0.2.0)
Per-tick latency	508 ms (p95)	28 ms
Speedup	1×	18×
Inference count over 65 min soak	9 769	9 769 (parallel, additive)
Tensor arena	256 KB	comfortably under, in PSRAM

Currently deployed in observation-only mode — the teacher is still on the critical inference path while we collect concurrent teacher/student traces for calibration. The 28 ms / 18× headroom means the student alone could sustain ~30 fps inference if the teacher is removed from the loop, vs. the current teacher-bound ~1.75 fps.

The 0.96 s log-mel patch is computed once on-device per tick and shared with both models, so the 28 ms figure is the TFLite-Micro inference cost only — pre-processing is paid by the shared front-end. PSRAM availability (8 MB on the WS-ESP32-S3-CAM unit) means the tensor arena lives off-chip; SRAM pressure stays low.

Limitations

Per-deployment threshold calibration is required. The naive 0.5 threshold yields F1 = 0.19. The right operating point is in the 0.03–0.05 range and shifts with sensor gain, room acoustics, and ambient noise floor.
Single-household training. Captures come from one deployment site; generalization to wildly different nursery acoustics (multiple infants, hardwood-heavy reverb, outdoor) is not characterized in this release.
96 % speech contamination in captures. Caregiver speech is co-present with cry in nearly all positive captures from the augmentation pool. AudioSet supplies clean cries to mitigate, but the model has not been benchmarked in purely speech-free environments.
Test set decay. AudioSet test segments are referenced by YouTube ID — 38 % were unreachable at quantization time (May 2026). Re-running the eval against today's YouTube state may produce slightly different numbers as further takedowns occur.
INT8 rounds extremes. Whisper-quiet cries (RMS < 200) may register zero probability mass even when the FP32 teacher detects them.

Citation

bibtex

@misc{yamnet-cry-distill-int8,
  author = {Orapinpatipat, Chayut},
  title = {yamnet-cry-distill-int8: a distilled INT8 baby-cry detector for ESP32-S3},
  year = {2026},
  howpublished = {\url{https://huggingface.co/chayuto/yamnet-cry-distill-int8}},
}

License

MIT. See LICENSE in the source repo.

Teacher artifact: chayuto/yamnet-mel-int8-tflm — INT8 YAMNet with documented mel-magnitude correction, also for ESP32-S3.
Source repo: chayuto/yamnet-cry-distill-int8.
Sibling firmware: chayuto/ws-ESP32-S3-CAM.

chayuto/yamnet-cry-distill-int8

yamnet-cry-distill-int8