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WHINY: Audio Pattern Recognition of Crying Baby

Supervised By:
Imran Ihsan
2019 - 2020
Project completed in 100%.
Feature Extraction
Cross Validated Results
Finding Outliers

Project description

Classification of Infants Cry Vocalizations using Machine Learning

The originality of the proposed approach lies in its ability to detect and analyze automatically distress signals, which recurrently affects 20 to 25% of newborns. Different types of cries have different time domain, frequency domain, and energy spectrum. The difference of these acoustic parameters provides the basis for the realization of sound classification. In our project, the Mel frequency cepstral coefficients of the voiced segment which is used as the primary characteristics of the signal. The classification is performed using ensemble learning methods after a stage of feature selection. The utilization of pre-crying signals to enhance the quality of the recognition is another important aspect of the proposed approach, which optimizes the accuracy of the learning step as it is shown by the obtained results on the collected dataset. This result gives the opportunity to develop new baby monitors able to predict the infantís needs. In addition, because the cry of the baby may con

Tools and Technologies

Project tag