Machine anomalous sound detection based on audio synthesis generative adversarial network

Abstract In the pursuit of implementing safety monitoring within Industry 4.0 smart production, deep learning methodologies are employed to harness the audio signals emanating from the operation of machinery and equipment, facilitating anomaly detection. Aiming to address the challenge of the few-shot samples for anomalous sound detection in an unsupervised setting, this paper introduces a machine anomalous sound detection method using an Audio Synthesis Adversarial Generative Network (ASGAN). The augmentation of classifiers within the network’s discriminator is proposed to execute a multi-classification task, thereby transforming the unsupervised training milieu into a semi-supervised one and enhancing the discriminator’s accuracy. To address the oversight of audio frequency domain features by conventional generative adversarial networks, the squeeze-and-excitation network is applied to concentrate on crucial frequency bands within the frequency domain. Experimental validation is conducted by using the DCASE Challenge Task2 dataset, revealing that the performance of the anomaly detection method leveraging audio synthesis generative adversarial networks surpasses that of the baseline system.