  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.
