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Multi-branch Network with Cross-Domain Feature Fusion for Anomalous Sound Detection

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Man-Machine Speech Communication (NCMMSC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2006))

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Abstract

Anomalous sound detection (ASD) is a key technology to identify abnormal sounds in various industries. Self-supervised anomalous sound detection aims at detecting unknown machine anomalous sounds by learning the characteristics of the normal sounds using metainformation. In this paper, we propose a multi-branch network with cross-domain feature fusion (MBN-CFF) for self-supervised ASD task. The multi-branch network splits the complete feature representations and feeds them individually into classifiers to generate category predictions. The weighted loss, calculated by multiple predictions and the real labels, guides the model training process. We also design a cross-domain feature fusion (CFF) block for effectively fusing the time-domain and frequency-domain features and an attentive sandglass (AS) block for effectively extracting features. Experimental results on the DCASE2020 challenge task 2 show that our MBN-CFF network achieves the best performance with the AUC score of 94.73% and pAUC score of 88.74%, respectively, compared to the other five existing methods for anomalous sound detection. The results of ablation experiments show the effectiveness of CFF and AS blocks, multi-brach prediction (MBP).

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Acknowledgements

This work is supported by the Multi-lingual Information Technology Research Center of **njiang (ZDI145-21).

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Authors and Affiliations

  1. School of Computer Science and Technology, **njiang University, Urumqi, China

    Wenjie Fang, **n Fan & Ying Hu

  2. Key Laboratory of signal detection and processing in **njiang, Urumqi, China

    Wenjie Fang, **n Fan & Ying Hu

Authors
  1. Wenjie Fang
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  2. **n Fan
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  3. Ying Hu
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Corresponding author

Correspondence to Ying Hu .

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Editors and Affiliations

  1. Tsinghua University, Bei**g, China

    Jia Jia

  2. University of Science and Technology of China, Anhui, China

    Zhenhua Ling

  3. Shanghai Jiao Tong University, Shanghai, China

    **e Chen

  4. Bei**g University of Posts and Telecommunications, Bei**g, China

    Ya Li

  5. Hunan University, Hunan, China

    Zixing Zhang

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Fang, W., Fan, X., Hu, Y. (2024). Multi-branch Network with Cross-Domain Feature Fusion for Anomalous Sound Detection. In: Jia, J., Ling, Z., Chen, X., Li, Y., Zhang, Z. (eds) Man-Machine Speech Communication. NCMMSC 2023. Communications in Computer and Information Science, vol 2006. Springer, Singapore. https://doi.org/10.1007/978-981-97-0601-3_18

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  • DOI: https://doi.org/10.1007/978-981-97-0601-3_18

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