Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

TL;DR

This paper discusses the DCASE 2020 Challenge Task 2, focusing on unsupervised anomalous sound detection for machine condition monitoring, highlighting new approaches and their challenges based on a large-scale benchmark dataset.

Contribution

It introduces the first benchmark for ASD research, including dataset, metrics, baseline, and analysis of novel approaches from multiple teams.

Findings

117 submissions from 40 teams analyzed

Two new approaches discussed and their issues identified

Benchmark dataset and evaluation metrics established

Abstract

In this paper, we present the task description and discuss the results of the DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring. The goal of anomalous sound detection (ASD) is to identify whether the sound emitted from a target machine is normal or anomalous. The main challenge of this task is to detect unknown anomalous sounds under the condition that only normal sound samples have been provided as training data. We have designed this challenge as the first benchmark of ASD research, which includes a large-scale dataset, evaluation metrics, and a simple baseline system. We received 117 submissions from 40 teams, and several novel approaches have been developed as a result of this challenge. On the basis of the analysis of the evaluation results, we discuss two new approaches and their problems.