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The safety of children in children home has become an increasing socialconcern, and the purpose of this experiment is to use machine learning appliedto detect the scenarios of child abuse to increase the safety of children. Thisexperiment uses machine learning to classify and recognize a child's voice andpredict whether the current sound made by the child is crying, screaming orlaughing. If a child is found to be crying or screaming, an alert isimmediately sent to the relevant personnel so that they can perceive what thechild may be experiencing in a surveillance blind spot and respond in a timelymanner. Together with a hybrid use of video image classification, the accuracyof child abuse detection can be significantly increased. This greatly reducesthe likelihood that a child will receive violent abuse in the nursery andallows personnel to stop an imminent or incipient child abuse incident in time.The datasets collected from this experiment is entirely from sounds recorded onsite at the children home, including crying, laughing, screaming sound andbackground noises. These sound files are transformed into spectrograms usingShort-Time Fourier Transform, and then these image data are imported into a CNNneural network for classification, and the final trained model can achieve anaccuracy of about 92% for sound detection.

Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU device