Outlier-aware Inlier Modeling and Multi-scale Scoring for Anomalous Sound Detection via Multitask Learning

TL;DR

This paper introduces a multitask learning framework that combines outlier exposure and inlier modeling for more robust and effective anomalous sound detection, utilizing multi-scale scoring.

Contribution

It presents a novel multitask learning approach with a conformer encoder that unifies outlier-aware inlier modeling and multi-scale scoring, improving detection performance.

Findings

Outperforms state-of-the-art single-model systems

Achieves comparable results with top multi-system ensembles

Demonstrates robustness across multiple datasets

Abstract

This paper proposes an approach for anomalous sound detection that incorporates outlier exposure and inlier modeling within a unified framework by multitask learning. While outlier exposure-based methods can extract features efficiently, it is not robust. Inlier modeling is good at generating robust features, but the features are not very effective. Recently, serial approaches are proposed to combine these two methods, but it still requires a separate training step for normal data modeling. To overcome these limitations, we use multitask learning to train a conformer-based encoder for outlier-aware inlier modeling. Moreover, our approach provides multi-scale scores for detecting anomalies. Experimental results on the MIMII and DCASE 2020 task 2 datasets show that our approach outperforms state-of-the-art single-model systems and achieves comparable results with top-ranked multi-system ensembles.