  Detection of baby cries is an important part of baby monitoring and health
care. Almost all existing methods use supervised SVM, CNN, or their varieties.
In this work, we propose to use weakly supervised anomaly detection to detect a
baby cry. In this weak supervision, we only need weak annotation if there is a
cry in an audio file. We design a data mining technique using the pre-trained
VGGish feature extractor and an anomaly detection network on long untrimmed
audio files. The obtained datasets are used to train a simple CNN feature
network for cry/non-cry classification. This CNN is then used as a feature
extractor in an anomaly detection framework to achieve better cry detection
performance.
