REAL-TIME SOUND ANOMALY DETECTION OF INDUSTRIALENVIRONMENTS WITH DEEP LEARNING

Abstract

In response to the increasing demand for enhanced industrial
safety and efficiency, this research delves into the domain of sound anomaly
detection within industrial environments, leveraging the power of deep learning.
Focused on addressing the limitations of traditional methods, the study
investigates various deep learning architectures, including convolutional neural
networks (CNNs), recurrent neural networks (RNNs), and hybrid models, to
discern their efficacy in detecting abnormal sounds. The survey rigorously
evaluates datasets, preprocessing techniques, and benchmarks, providing a
comprehensive overview of the state-of-the-art models and their applications
across diverse industrial sectors.
The paper scrutinizes performance evaluation metrics, drawing
comparisons between deep learning and traditional methods in sound anomaly
detection. Real-world applications and case studies underscore the practical
significance of these advancements. While acknowledging achievements, the
research identifies challenges and proposes future directions, emphasizing the
need for innovative solutions to enhance the robustness and real-world
applicability of deep learning-based sound anomaly detection in industrial
settings.
This research not only contributes valuable insights into the intersection
of deep learning and industrial sound analysis but also serves as a pivotal guide
for researchers and practitioners seeking to navigate the complexities of
deploying effective sound anomaly detection systems.