  Most existing cry detection models have been tested with data collected in
controlled settings. Thus, the extent to which they generalize to noisy and
lived environments is unclear. In this paper, we evaluate several established
machine learning approaches including a model leveraging both deep spectrum and
acoustic features. This model was able to recognize crying events with F1 score
0.613 (Precision: 0.672, Recall: 0.552), showing improved external validity
over existing methods at cry detection in everyday real-world settings. As part
of our evaluation, we collect and annotate a novel dataset of infant crying
compiled from over 780 hours of labeled real-world audio data, captured via
recorders worn by infants in their homes, which we make publicly available. Our
findings confirm that a cry detection model trained on in-lab data
underperforms when presented with real-world data (in-lab test F1: 0.656,
real-world test F1: 0.236), highlighting the value of our new dataset and
model.
