mlsanityData to 7 Oct 2026

Anomalous Sound Detection with Machine Learning: A Systematic Review

arXiv 2102.07820 ↗First posted 15 Feb 2021cs.SD cs.AI cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 10 of its 49 references that are in the map. First posted on arXiv 15 Feb 2021.

Builds on

10 of 49 references.

References · 10

  1. 2018A multi-device dataset for urban acoustic scene classificationcited by 75
  2. 2019MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspectioncited by 57
  3. 2019ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detectioncited by 43
  4. 2018General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baselinecited by 30
  5. 2018Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemmacited by 19
  6. 2020Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examplescited by 10
  7. 2018Sound Event Detection and Time-Frequency Segmentation from Weakly Labelled Datacited by 9
  8. 2019Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Soundscited by 8
  9. 2020ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detectioncited by 7
  10. 2020Anomalous Sound Detection using unsupervised and semi-supervised autoencoders and gammatone audio representationcited by 3

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 2.

5 papers.

  1. 2024Machine Anomalous Sound Detection Using Spectral-temporal Modulation Representations Derived from Machine-specific Filterbankscited by 2
  2. 2025Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planerscited by 2
  3. 2023Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machinescited by 1
  4. 2022Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motorscited by 0
  5. 2025Contrastive Learning with Spectrum Information Augmentation in Abnormal Sound Detectioncited by 0