RansomListener: Ransom Call Sound Investigation Using LSTM and CNN Architectures

Getting calls for ransoms is the common phenomena involved in kidnapping and abduction related activities, where the life of a victim remains extremely vulnerable. These phone calls are often analyzed in real-time by law enforcement authorities to quickly identify the suspects and get crucial information for quick action. However, it is often difficult to manually analyze those phone calls due to the quality of sounds and the presence of several background noises. Even with more high-end software in their inventory, it is futile to accurately refine the incoming calls as it takes a huge amount of time to declutter the different layers of noises in the call. This paper proposes a model based on deep convolutional neural network and signal processing for the automatic classification of crucial sounds in ransom related phone calls. This research work has proposed LSTM and 2D CNN customized models and compared their outputs with VGG16 and AlexNet. Moreover, this paper also presents a unique dataset of different sounds in terms of voices like male or female and the environmental sounds where the victim might be in which can be a probable clue for investigation purposes that consist of 17650 audio clips collected from verified online sources. Finally, the models have produced very high classification accuracy along with the accuracy of LSTM reaching around 93.4%.