TR2022-092
Disentangled surrogate task learning for improved domain generalization in unsupervised anomolous sound detection
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- "Disentangled surrogate task learning for improved domain generalization in unsupervised anomolous sound detection," Tech. Rep. TR2022-092, DCASE2022 Challenge, July 2022.BibTeX TR2022-092 PDF
- @techreport{Venkatesh2022jul,
- author = {Venkatesh, Satvik and Wichern, Gordon and Subramanian, Aswin Shanmugam and Le Roux, Jonathan},
- title = {Disentangled surrogate task learning for improved domain generalization in unsupervised anomolous sound detection},
- institution = {DCASE2022 Challenge},
- year = 2022,
- month = jul,
- url = {https://www.merl.com/publications/TR2022-092}
- }
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- "Disentangled surrogate task learning for improved domain generalization in unsupervised anomolous sound detection," Tech. Rep. TR2022-092, DCASE2022 Challenge, July 2022.
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Research Areas:
Abstract:
We present our submission to the DCASE2022 Challenge Task 2, which focuses on domain generalization for anomalous sound detection. We investigated a novel multitask learning framework that disentangles domain shared features and domain-specific features. Disentanglement leads to better latent features and also increases flexibility in post-processing due to the availability of multiple embedding spaces. Our disentangled model obtains an overall harmonic mean of 74.57% on the development set, surpassing the MobileNetV2 baseline, which obtains 56.01%. Lastly, we explore the use of machine-specific loss functions and domain generalization methods, which improves our overall performance to 76.42%.