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.

翻译：儿童之家中的儿童安全问题日益成为社会关注的焦点，本实验旨在运用机器学习技术检测儿童虐待场景，以提升儿童安全保障水平。实验采用机器学习方法对儿童声音进行分类识别，预测当前儿童发出的声音是哭泣、尖叫还是笑声。若检测到儿童处于哭泣或尖叫状态，系统将立即向相关人员发送警报，使其能够感知监控盲区中儿童可能遭遇的情况并及时响应。结合视频图像分类的混合应用，可显著提升儿童虐待检测的准确率，从而大幅降低儿童在托育机构中遭受暴力侵害的可能性，使工作人员能够及时制止即将发生或初期的儿童虐待事件。本实验采集的数据集完全来自儿童之家现场录制的声音，包括哭泣、笑声、尖叫声及背景噪声。通过短时傅里叶变换将声音文件转化为频谱图后，将这些图像数据导入CNN神经网络进行分类，最终训练模型在声音检测方面可实现约92%的准确率。