Machine Diagnosis by Anomalous Sound Detection Using AI Techniques

Detecting abnormal sounds is crucial in various fields like security, environmental monitoring, and health surveillance. This proposal presents a method for identifying machine anomalous sounds using machine learning and unsupervised learning techniques. Our methodology includes data preprocessing, feature extraction, and training a deep neural network using a pre-trained AutoML (Automated Machine Learning) service to differentiate between normal and anomalous sounds. The system strives for real-time, high-accuracy detection, making it valuable across applications. This research addresses current limitations in sound anomaly detection, aiming to significantly impact the field. Our experiments demonstrate that our method surpasses state-of-the-art techniques, including pre-trained learning and self-supervised classification, in both overall anomaly detection performance and stability on the DCASE 2020 Challenge Task2 dataset.