audio-alert-detector
Tiny CNN that classifies a 10-second audio clip as alert (siren, smoke/fire alarm, car alarm, house/burglar alarm) vs not-alert.
Designed to run on a Raspberry Pi Zero 2 in the ONNX runtime with no accelerator.
Model
- Depthwise-separable CNN (~35k params, ~17M MACs)
- Input: raw mono 16 kHz PCM, 10 s = 160k samples
- Embedded log-mel frontend (no host-side preprocessing needed beyond resampling to 16kHz)
- Two heads: binary (alert/not) + auxiliary subclass (siren/alarm)
- Δ + ΔΔ time-derivative input channels for onset/sweep dynamics
Training data
- ~11k positives + ~60k negatives, 10 s each, 16 kHz mono
- AudioSet via confit/audioset-full HF mirror
- Targeted hard-negative mid lists (bells, whistles, woodwinds, mechanical, instruments, animals, music) for known FP categories
- Curated supplemental positives: ~199 country-specific EAS alarms + ~107 nuclear/civil-defense sirens
Training
- 40 epochs, AdamW (lr 3e-4, wd 1e-4), cosine LR schedule
- Batch 64, fixed 40% positive fraction per batch (uniform within each pool)
- Loss: binary BCE + 0.3 × subclass CE (masked to positives)
Augmentation (mel-space)
- Random time-stretch (0.9–1.1×)
- Random gain (–45 to +15 dB)
- Frequency shift (±4 mel bins ≈ ±2 semitones)
- Companding (γ 0.75–1.25, p=0.3)
- SpecAugment time + freq masks
- Curated ambience overlay (rain / cafe / road traffic / mic noise floor / etc.) at 25% RMS, applied to both classes
Deployment
Single-file fp32 ONNX (348 KB). Input: float32[batch, 160000] raw 16 kHz mono PCM. Outputs: binary_logit + subclass_logits[2]. Apply sigmoid for alert probability.
Recommended: 10s ring buffer, infer every 2 s, threshold 0.5, require 2 consecutive over-threshold windows to fire (eliminates almost all single-window FPs).
Performance
- Test set (225 curated clips): 88% acc / 89% prec / 83% rec / F1 0.86
- Pi Zero 2 (Cortex-A53, fp32, single thread): ~150 ms / 10 s window
