# A Fast Identification Method of Gunshot Types Based on Knowledge Distillation
## 1 Introduction  
Gun violence is a daily tragedy that affects the lives of individuals around the world. More than 500 people die every day as a result of gun violence, and 44% of worldwide homicides involve gun violence. Millions of people suffer from the severe and long-term psychological effects of gun violence or the threat of gun violence on individuals, families and their wider communities. Identifying the types of criminal firearms in time can effectively reduce casualties. The identification of the guns held by criminal suspects can help the police determine the threat level of criminal suspects and formulate effective arrest plans. For treating people with gunshot wounds, it can help medical staff to judge the patient's injury and implement targeted treatment by knowing the type of gun. Moreover, gunshot recognition systems can also be used as a complement to daily video surveillance to jointly monitor the occurrence of gun violence. In order to realize the above functions, the gunshot type recognition method needs to meet the requirements of real-time and lightweight. And unlike common audio monitoring and sound event recognition, sniper gunshots of various calibers and types are difficult to identify due to their similar sound spectrogram characteristics. Researchers have done a lot of work on gunshot recognition.  
[PERSON] and [PERSON] [1] proposed a forensic audio gunshot analysis method which tries to derive, from the audio produced by live gunfire, important information about the caliber, model, and type of guns used by criminals. [PERSON] et al. [2] proposed a gunshot recognition system based on support vector machine (SVM) to determine whether an audio signal carries a gunshot, achieving an accuracy rate of 70.39%. [PERSON] et al. [3] combined Mel-Frequency Cepstral Coefficients (MFCC) with SVM, effectively improving the accuracy of gunshot recognition. [PERSON] and [PERSON] [4] applied the GMM classifier to gunshotclassification, seeking to classify existing gunshot signals out of the noisy environment, and experiments demonstrated that the classification of gunshot signals of five different calibers can reach 96.29% in accuracy. [PERSON] et al. [5] improved on the GMM classifier by embedding samples in it and registered an accuracy of 60-72% in an experiment covering a total of 100 shots of 20 different types. [PERSON] et al. [6] proposed a decoding algorithm based on Hidden Markov Models (HMM) and Viterbi, which tries to obtain audio signals from urban noise monitoring systems and then determine the presence or absence of gunshots, and an 80% successful detection rate was achieved. With the development of deep learning technology, neural networks have also found applications in gunshot detection and recognition. [PERSON] [7] applied transfer learning to gunshot recognition--the 14-layer CNN network was pre-trained on the large Audioset, and then the resulting model was trained on 6000 individual gunshots of 18 types of guns. A 78.2% accuracy was registered. [PERSON] and [PERSON] [8] proposed a gunshot classification method based on convolutional neural networks; 3655 pieces of audio from 59 different weapons were classified, giving a 90% accuracy.  
In summary, with the continuous improvement in gunshot recognition accuracy, the network model becomes deeper, more training parameters are involved, and there is a greater demand on hardware computing power, leading to a decrease in the speed of gunshot recognition and to insufficient real-time performance. Consequently, it is difficult to achieve miniaturization. To address the above problems, this paper proposes a gunshot recognition method based on knowledge distillation. The scale of the network model is thus reduced without harming the accuracy of gunshot recognition, which lowers the requirement for the computing power of the network so that it can be used in embedded devices.