Audio Pattern Recognition of Baby Crying Sound Events

Ntalampiras S. Audio Pattern Recognition of Baby Crying Sound Events. J Audio Eng Soc. 2015;63(5):358-369. doi:10.17743/jaes.2015.0025

@article{Ntalampiras2015_17641,
  author = {Ntalampiras, Stavros},
  title = {{Audio Pattern Recognition of Baby Crying Sound Events}},
  journal = {Journal of the Audio Engineering Society},
  volume = {63},
  number = {5},
  pages = {358--369},
  year = {2015},
  month = may,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2015.0025},
  url = {https://doi.org/10.17743/jaes.2015.0025}
}

TY  - JOUR
TI  - Audio Pattern Recognition of Baby Crying Sound Events
AU  - Ntalampiras, Stavros
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 63
IS  - 5
SP  - 358
EP  - 369
PY  - 2015
DA  - 2015/05/06
DO  - 10.17743/jaes.2015.0025
UR  - https://doi.org/10.17743/jaes.2015.0025
PB  - Audio Engineering Society
LA  - en
AB  - Infants can communicate their internal state (such as pain, hunger, fear, fatigue, or stress) by the nature of their crying. Experts in linguistics suggest that the cry comprises the first speech manifestations. This article describes the design methodology for classifying baby crying sound events according to the pathological status of the infant. Such an automated system can be an aid to an attending physician performing a diagnosis. In order to address this challenge, a great variety of audio parameters (Perceptual Linear Prediction, Mel Frequency Cepstral Coefficients, Perceptual Wavelet Packets, Teager Energy Operator, Temporal Modulation) were considered. Classification techniques, including Multilayer Perception, Support Vector Machine, Random Forest, Reservoir Network, Gaussian Mixture model, and Hidden Markov model were customized. The goal is to provide an automatic and noninvasive framework for monitoring infants and helping inexperienced/trainee pediatricians, parents, and baby caregivers to identify the baby’s pathological status.
ER  -