# Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples

arXiv: https://arxiv.org/abs/2011.02949 · HTML: https://mlsanity.com/paper/2011.02949/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task](https://mlsanity.com/paper/2205.13879/) (2022)
- [Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques](https://mlsanity.com/paper/2206.05876/) (2022)
- [Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions](https://mlsanity.com/paper/2106.04492/) (2021)
- [MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions](https://mlsanity.com/paper/2105.02702/) (2021)
- [Anomalous Sound Detection with Machine Learning: A Systematic Review](https://mlsanity.com/paper/2102.07820/) (2021)
- [Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?](https://mlsanity.com/paper/2309.15643/) (2023)
- [Multimedia Datasets for Anomaly Detection: A Review](https://mlsanity.com/paper/2112.05410/) (2021)
- [Anomalous Sound Detection Based on Machine Activity Detection](https://mlsanity.com/paper/2204.07353/) (2022)
- [Anomalous Sound Detection Using a Binary Classification Model and Class Centroids](https://mlsanity.com/paper/2106.06151/) (2021)
- [Constraint Guided AutoEncoders for Joint Optimization of Condition Indicator Estimation and Anomaly Detection in Machine Condition Monitoring](https://mlsanity.com/paper/2409.11807/) (2024)

## Abstract (arXiv)

Unsupervised anomalous sound detection is concerned with identifying sounds that deviate from what is defined as 'normal', without explicitly specifying the types of anomalies. A significant obstacle is the diversity and rareness of outliers, which typically prevent us from collecting a representative set of anomalous sounds. As a consequence, most anomaly detection methods use unsupervised rather than supervised machine learning methods. Nevertheless, we will show that anomalous sound detection can be effectively framed as a supervised classification problem if the set of anomalous samples is carefully substituted with what we call proxy outliers. Candidates for proxy outliers are available in abundance as they potentially include all recordings that are neither normal nor abnormal sounds. We experiment with the machine condition monitoring data set of the 2020's DCASE Challenge and find proxy outliers with matching recording conditions and high similarity to the target sounds particularly informative. If no data with similar sounds and matching recording conditions is available, data sets with a larger diversity in these two dimensions are preferable. Our models based on supervised training with proxy outliers achieved rank three in Task 2 of the DCASE2020 Challenge.