Detection of Children Abuse by Voice and Audio Classification by
  Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU
  device
Jiuqi Yan Yingxian Chen W. W. T. Fok
The safety of children in children home has become an increasing social
concern, and the purpose of this experiment is to use machine learning applied
to detect the scenarios of child abuse to increase the safety of children. This
experiment uses machine learning to classify and recognize a child's voice and
predict whether the current sound made by the child is crying, screaming or
laughing. If a child is found to be crying or screaming, an alert is
immediately sent to the relevant personnel so that they can perceive what the
child may be experiencing in a surveillance blind spot and respond in a timely
manner. Together with a hybrid use of video image classification, the accuracy
of child abuse detection can be significantly increased. This greatly reduces
the likelihood that a child will receive violent abuse in the nursery and
allows personnel to stop an imminent or incipient child abuse incident in time.
The datasets collected from this experiment is entirely from sounds recorded on
site at the children home, including crying, laughing, screaming sound and
background noises. These sound files are transformed into spectrograms using
Short-Time Fourier Transform, and then these image data are imported into a CNN
neural network for classification, and the final trained model can achieve an
accuracy of about 92% for sound detection.