Spectral Vector Design for Gunfire Sound Classification System with a Smartphone using ANN

In this research, a system for classifying the gunfire sound has been studied. The system is designed to function with a smartphone, which has a limited resource. The input sound is converted from the analog format to the digital one. The digital gunfire sound is then processed and analyzed in frequency domain using the smartphone. It is shown that, with noise injection method, using Artificial Neural Network (or ANN) in the classification process, the obtained accuracy for classifying 6 different gunfire sounds is considerably increased compared to the results found in [1]. Additionally, in this work, the feature vector with different number of bins in frequency domain is deeply studied. It is found that with a proper number of bins in the classification, the classification accuracy is significantly improved. The 100%-accuracy can be achieved for the SNR down to 10 dB and a very high accuracy (that is, greater than 90%) can be obtained at the 0-dB SNR. Using an appropriate number of feature vectors can lead to a promising performance in terms of gunfire sound classification.