Development of a system for notification of dangerous sounds using machine learning methods

Abstract

The rapid urbanization of cities has resulted in increased noise pollution and the prevalence of sounds that can signify dangerous situations, such as gunshots, screams, and alarms. Traditional methods of sound detection often fall short in accuracy and efficiency, leading to delays in emergency responses and increased risks to public safety. This paper presents the development of a machine learning-based system designed to identify and notify authorities about dangerous sounds in real-time. Through data collection, feature extraction, model training, and the implementation of a notification system, we aim to create a reliable solution that enhances public safety. This study also addresses ethical considerations related to privacy and algorithmic bias, ensuring that the proposed system is both effective and responsible.