GUN SOUND RECOGNITION USING NLP AND YAMNET MODEL

Abstract

This presents a hybrid approach to gunshot sound detection by integrating Mel-Frequency Cepstral Coefficients (MFCC), Support Vector Machines (SVM), and YAMNet, a pretrained deep learning model. The process begins with the extraction of MFCC features from audio data, which capture the essential characteristics of the sound spectrum. These features are then used to train an SVM model to classify sounds as gunshots or nongunshots. To enhance detection accuracy, YAMNet is employed to classify the audio into a broader range of categories, providing an additional layer of validation or complementing the SVM's predictions. The combination of SVM's precision with YAMNet's extensive sound classification capabilities results in a robust system capable of accurately identifying gunshot sounds in real-time audio streams. This hybrid approach leverages both traditional machine learning and state-of-the-art deep learning techniques, offering a reliable solution for gunshot detection in various applications. Index Terms – Gun Sound Recognition, YAMNet, Audio Classification, Sound Event Detection, Deep Learning, Acoustic Signal Processing, Machine Learning, RealTime Detection, Public Safety, Artificial Intelligence.