# Anomalous Sound Detection with Machine Learning: A Systematic Review

arXiv: https://arxiv.org/abs/2102.07820 · HTML: https://mlsanity.com/paper/2102.07820/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [A multi-device dataset for urban acoustic scene classification](https://mlsanity.com/paper/1807.09840/) (2018)
- [MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection](https://mlsanity.com/paper/1909.09347/) (2019)
- [ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection](https://mlsanity.com/paper/1908.03299/) (2019)
- [General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline](https://mlsanity.com/paper/1807.09902/) (2018)
- [Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma](https://mlsanity.com/paper/1810.09133/) (2018)
- [Sound Event Detection and Time-Frequency Segmentation from Weakly Labelled Data](https://mlsanity.com/paper/1804.04715/) (2018)
- [Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples](https://mlsanity.com/paper/2011.02949/) (2020)
- [Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds](https://mlsanity.com/paper/1907.08338/) (2019)
- [ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2007.05314/) (2020)
- [Anomalous Sound Detection using unsupervised and semi-supervised autoencoders and gammatone audio representation](https://mlsanity.com/paper/2006.15321/) (2020)

## Built on by (most cited first, 5 of 5)

- [Machine Anomalous Sound Detection Using Spectral-temporal Modulation Representations Derived from Machine-specific Filterbanks](https://mlsanity.com/paper/2409.05319/) (2024)
- [Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers](https://mlsanity.com/paper/2501.04819/) (2025)
- [Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machines](https://mlsanity.com/paper/2312.01062/) (2023)
- [Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motors](https://mlsanity.com/paper/2211.01716/) (2022)
- [Contrastive Learning with Spectrum Information Augmentation in Abnormal Sound Detection](https://mlsanity.com/paper/2509.15570/) (2025)

## 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.