Enhanced Gunshot Sound Detection using AlexNet and XGBoost from Fourier Spectrograms
M. Yagnasri Priya, Sanjivani P. Shendre, Peeta Basa Pati · 2024
Gunshot detection and classification are pivotal for ensuring public safety, law enforcement, defense, and forensic investigations. This paper presents a comparitive study into the effectiveness of deep learning and traditional machine learning models in classifying gunshot sounds through Short-Term Fourier Transform (STFT) spectograms is presented. The study involves integrating Convolutional Neural Networks (CNNs) with standard machine learning techniques to extract and analyze features, particularly focusing on the penultimate layer of CNNs. By leveraging these features, this paper aims to enhance the system’s ability to accurately identify complex sound patterns associated with gunshots. This analysis provides insights into the strengths and weaknesses of deep learning and machine learning approaches in gunshot classification, particularly achieving a perfect accuracy score with AlexNet and XGBoost. These findings pave the way for more effective gunshot detection systems in a variety of operational scenarios, thereby significantly contributing to public safety measures.