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Robust Prototype Learning for Anomalous Sound Detection

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conference contribution
posted on 2023-10-01, 00:58 authored by Xiao-Min Zeng, Yan Song, Ian McLoughlinIan McLoughlin, Lin Liu, Li-Rong Dai

In this paper, we present a robust prototype learning framework for anomalous sound detection (ASD), where prototypical loss is exploited to measure the similarity between samples and prototypes. We show that existing generative and discriminative based ASD methods can be unified into this framework from the perspective of prototypical learning. For ASD in recent DCASE challenges, extensions related to imbalanced learning are proposed to improve the robustness of prototypes learned from source and target domains. Specifically, balanced sampling and multiple-prototype expansion (MPE) strategies are proposed to address imbalances across attributes of source and target domains. Furthermore, a novel negative-prototype expansion (NPE) method is used to construct pseudo-anomalies to learn a more compact and effective embedding space for normal sounds. Evaluation on the DCASE2022 Task2 development dataset demonstrates the validity of the proposed prototype learning framework.

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Journal/Conference/Book title

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 20-24 August 2023, Dublin, Ireland

Publication date

2023-08-21

Version

  • Published

Rights statement

Zeng, X.-M., Song, Y., McLoughlin, I., Liu, L., Dai, L.-R. (2023) Robust Prototype Learning for Anomalous Sound Detection. Proc. INTERSPEECH 2023, 261-265, doi: 10.21437/Interspeech.2023-1173

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