Benchmarking Baby Cry Detection Algorithms: 2026 Edition

We evaluated 8 popular baby cry detection approaches across 5 real-world noise conditions. This article shares our methodology and findings.

Test Setup

Test Set — 12,000+ audio clips (6,000 cry, 6,000 non-cry)

Noise Conditions — Quiet, TV background, white noise, conversation, street noise

Metrics — Accuracy, Precision, Recall, F1 Score, False Alarm Rate

Results Summary

Key Findings

1. Lightweight CNNs with Mel-spectrogram input achieved the best accuracy-to-size ratio

2. Data augmentation (noise mixing, speed perturbation) improved generalization by 3-5%

3. Quantization (FP32 → INT8) reduced model size by 4x with < 1% accuracy loss

4. TV background noise was the most challenging condition across all methods

Recommendations

For embedded devices (ARM Cortex-A class), we recommend MFCC + lightweight CNN with INT8 quantization. This provides > 95% accuracy at < 1.5MB model size.