Contrastive Learning with Spectrum Information Augmentation in Abnormal Sound Detection
Article No.: 59, Pages 1 - 5
Abstract
The outlier exposure method is an effective approach to address the unsupervised anomaly sound detection problem. The key focus of this method is how to make the model learn the distribution space of normal data. Based on biological perception and data analysis, it is found that anomalous audio and noise often have higher frequencies. Therefore, we propose a data augmentation method for high-frequency information in contrastive learning. This enables the model to pay more attention to the low-frequency information of the audio, which represents the normal operational mode of the machine. We evaluated the proposed method on the DCASE 2020 Task 2. The results showed that our method outperformed other contrastive learning methods used on this dataset. We also evaluated the generalizability of our method on the DCASE 2022 Task 2 dataset.
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Index Terms
- Contrastive Learning with Spectrum Information Augmentation in Abnormal Sound Detection
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Published In
April 2024
373 pages
ISBN:9798400716607
DOI:10.1145/3663976
Copyright © 2024 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 June 2024
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CVIPPR 2024
CVIPPR 2024: 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition
April 26 - 28, 2024
Xiamen, China
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Overall Acceptance Rate 14 of 38 submissions, 37%
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