Domestic Violence Detection Using Smart Microphones

Domestic violence between partners and family members is a worldwide problem increasing every day. As per academic studies and media articles, it escalated during the COVID-19 outbreak. Domestic violence can portray verbally and physically in several ways (for instance, between partners or against children and older people). Deep Learning (DL) combined with the Internet of Things (IoT) technology could support the detection of domestic violence, which is one of many societal issues. This paper describes a system that uses a Deep Learning model and smart microphones to identify domestic violence. The datasets used are from the Google AudioSet (GA) and from the Toronto Emotional Speech Set (TESS). For the training of the dataset, the system used spectrograms and MFCCs (Mel-Frequency Cepstral Coefficients). The system employs two approaches: (i) an ANN (Artificial Neural Network) model, and (ii) a ResNet model. The Resnet model obtained an accuracy of 71%. The ANN model, which brought an accuracy of 83%, was tested and loaded to a Raspberry Pi, i.e., connected to the microphone for audio recording. The recorded audio was fed to the trained model, classifying the audio, and alerting the domestic violence to relatives, friends, or volunteers registered with the system via e-mail. The designed system is compact and can be placed inside the home.