Infant cry signal detection, pattern extraction and recognition

The cry signals generated by infants serves as the primary communication for infants. Cry signals can provide insight into their wellbeing. This paper proposes to use the speech signal identification technique to recognize infant cry signals. Advanced signal processing methods are used to analyze the infant cry by using audio features in the time and frequency domains in an attempt to classify each cry to a specific need. The features extracted from audio feature space include linear predictive coding (LPC), linear predictive cepstral coefficients (LPCC), Bark frequency cepstral coefficients (BFCC) and Mel frequency cepstral coefficients (MFCC). The primary classification technique used were: nearest neighbor approach, neural networks method. The cry recognition of specific infants yielded promising results.