Anomalous Sound Detection with Machine Learning: A Systematic Review

Abstract

Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from an object is normal or anomalous. In some cases, early detection of this anomaly can prevent several problems. This article presents a Systematic Review (SR) about studies related to Anamolous Sound Detection using Machine Learning (ML) techniques. This SR was conducted through a selection of 31 (accepted studies) studies published in journals and conferences between 2010 and 2020. The state of the art was addressed, collecting data sets, methods for extracting features in audio, ML models, and evaluation methods used for ASD. The results showed that the ToyADMOS, MIMII, and Mivia datasets, the Mel-frequency cepstral coefficients (MFCC) method for extracting features, the Autoencoder (AE) and Convolutional Neural Network (CNN) models of ML, the AUC and F1-score evaluation methods were most cited.

References in corpus (5)

• A multi-device dataset for urban acoustic scene classification
• General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline
• MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection
• Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples
• Anomalous Sound Detection using unsupervised and semi-supervised autoencoders and gammatone audio representation