For example, some “real-world” models appear include discrete samples of fans, music, speech, dogs barking, or doors closing. Negative examples may also be non-representative. ![]() For example, some real-world cry datasets were collected in home environments but only recorded discrete samples of crying rather than labelling crying from continuous audio recordings. ![]() First, most supposed “real-world” training data does not reflect the actual complexity of true real-world data. However, we argue that these models are unlikely to perform robustly in truly real-world settings. Recent reviews of infant crying detection/classification indicate that many published models achieve F1 scores over 0.9. Additionally, there is a lack of high-quality datasets that can be used to address this problem. In summary, there is a lack of robust models for detecting infant crying from real-world home settings. Comparing LENA against other tools and approaches is challenging since its training corpus, algorithms, and models are not made publicly available. While LENA has never released accuracy statistics on its cry detection models, recent work shows that its infant crying predictions have low accuracy relative to trained human coders. However, the main focus of LENA is to detect parent and child speech vocalizations. Researchers such as developmental psychologists have also relied on LENA (Language ENvironment Analysis), a commercial product, to capture and process relevant acoustic events - including infant crying - from continuous recordings collected in children’s everyday environments. Thus, synthetic datasets that layer additional sounds on clean laboratory datasets are not equivalent to real-world datasets. For example, the AUC of voice-activity detection trained and tested on a synthetic dataset was 5.27% higher than results of a model trained and tested on a real-world dataset. However, as demonstrated in other domains, models tested on synthetic datasets typically yield higher accuracy than those tested on real-world datasets. To attempt to mimic real-world conditions in training data, some papers have manually added sounds, such as from cars or medical equipment, to crying datasets collected in laboratory settings. ![]() In particular, the F1 score of cough detection dropped by 13.5% and the F1 score of laughter detection drops 20.7% percent when trained and tested on real-world vs. demonstrated that cough and laugh detection models performed poorly when trained and tested on real-world datasets relative to those trained and tested on in-lab datasets. The distinct challenges of detection and classification in real-world settings relative to clean-lab conditions have been illustrated in other domains. These additional sounds greatly increase the difficulty of detection and classification problems additionally, if such sounds are not present in the training data, performance will deteriorate in real-world conditions. By contrast, real-world environments, such as family households, typically include a variety of complex overlapping sounds that must be distinguished from behaviors of interest. Others are recorded in real-world settings but are trained and evaluated on short, pre-parsed segments containing non-overlapping individual sounds. However, many of these were developed and evaluated using data in controlled settings. Recently, a number of infant cry classification algorithms have been published. Further-more, such algorithms would be key to the development of applications to provide “just-in-time” support to caregivers. ![]() Developing automatic cry detection algorithms that perform robustly in everyday home settings is essential for basic science investigation of the links between crying, caregiving, and caregiver mental health. Crying is also a known stressor that can decrease caregiving quality and increase risks for infant development and caregiver mental health. Infant crying is a critical evolutionary signal that allows infants to communicate hunger, discomfort and pain.
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