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.

通过机器学习应用于儿童保育机构，本研究的目的是检测儿童虐待场景，以提高儿童的安全性，通过对儿童的声音进行分类和识别，预测儿童当前发出的声音是哭泣、尖叫还是笑声，并实时向相关人员发送警报，通过同时使用视频图像分类的混合方式，可以显著提高儿童虐待侦测的准确性，减少幼儿园儿童受到暴力虐待的可能性，使工作人员能及时干预即将发生或刚刚开始的儿童虐待事件。在该实验中收集的数据集完全来自儿童保育机构现场录制的声音，包括哭泣声、笑声、尖叫声和背景噪音，这些声音文件使用短时傅里叶变换转换为频谱图，然后将这些图像数据导入到一个 CNN 神经网络进行分类，最终训练的模型在声音检测方面可以达到约 92% 的准确率。