Enhancing Gun Detection With Transfer Learning and YAMNet Audio Classification

Abstract

Identification of the type of gun used is essential in several fields, including forensics, the military, and defense. In this research, one of the powerful deep learning architectures is applied to identify several types of firearms based on their gunshot noises. For the purpose of extracting features from the audio data, the suggested technique makes use of YAMNet, an effective deep learning-based classification model. The Mel spectrograms created from the collected features are used for multi-class audio classification, which makes it possible to identify different types of guns. 1174 audio samples from 12 distinct weapons make up the study's extensive dataset, which offers a varied and representative collection for training and evaluation. We achieve a remarkable accuracy of 94.96% by employing the best hyperparameter changes and optimization methods. The findings of this study make a substantial contribution to the domains of forensics, military, and defense, where precise gun type identification is crucial. Applying deep learning and mel spectrograms to analyze gunshot audio demonstrates itself to be a promising strategy, providing quick and accurate categorization. This research emphasizes the effectiveness and relevance of using YAMNet, an AI-driven model, as a superior answer to the issues of real-world weapon detection.