[home] [Personal Program] [Help]

---

Session: Behavior & Monitoring
Session starts: Thursday 24 January, 13:30
Presentation starts: 14:15
Room: Lecture room 535

---

Ahnjili ZhuParris (CHDR)
Max van Gent (CHDR)
Robert-Jan Doll (CHDR)
Geert Jan Groeneveld (CHDR)
Adam Cohen (CHDR)

Abstract:
The total duration and frequency of an infant’s cry each day can be indicative of a serious health condition. To track and quantify an infant’s crying behavior in challenging acoustic environments, a cry detection algorithm is needed. Here, we present and compare the performance of four types of classifiers to detect infant crying. A synthetic dataset was created containing about 400 five second recordings of either infants crying or common domestic sounds (e.g., infants laughing, people talking, and doors closing). We used the openSMILE software for feature extraction; features included pitch and energy related parameters (e.g., Mel-frequency Cepstral coefficients). Classification was done using four different classifiers: Logistic Regression, Random Forest, Support Vector Machines (SVM), and Multi-layer Perceptron classifier to correctly classify crying recordings from non-crying recordings. We then evaluated the hyper-parameter tuning of each classifier using 5 fold cross validation and assessed the predictive power of the network based on a 66% train and test set split. Here we present the design, implementation and comparison of these algorithms. The preliminary results reflect which audio features and algorithms produce the optimal sensitivity and selectivity for clinical application. The best results were obtained with the random forest and the least favorable results were obtained with the SVM. This approach of automating the classification of infant cries allows for the detection and quantification of crying frequency and duration which can then inform parents and physicians if an infant is in fact “crying excessively”. Our next step is to validate the predictive power of random forest to classify crying and non-crying audio recordings of infants in a hospital setting. The aim is to verify the accuracy of our infant cry detection classifier using recordings sourced from a mobile phone app in challenge acoustic environments.