Exploring Deep Learning Techniques in Impulsive Sound Detection Problem

Abstract

Impulsive sound detection, characterized by the identification of short-duration, high-intensity acoustic events, has broad applications in fields such as public safety, industrial monitoring, healthcare, and environmental conservation. Traditional approaches, relying on handcrafted features and signal processing methods, often struggle to handle the complexity and variability of impulsive sounds in real-world environments. This paper explores the application of deep learning techniques, specifically Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, to enhance detection performance. These models demonstrate the ability to automatically extract both spatial and temporal features from sound data, leading to improved accuracy in detecting impulsive sounds such as gunshots, explosions, and mechanical faults. Despite the advancements, challenges remain, including data scarcity, environmental noise, and computational costs. Addressing these limitations requires the development of more diverse datasets, advanced noise reduction techniques, and more efficient model architectures. This study highlights the potential of deep learning in providing scalable, real-time solutions for impulsive sound detection and offers insights into future research directions that could further refine the effectiveness and applicability of these systems across a wide range of practical contexts.