TR2022-146

IMPROVED DOMAIN GENERALIZATION VIA DISENTANGLED MULTI-TASK LEARNING IN UNSUPERVISED ANOMALOUS SOUND DETECTION


    •  Venkatesh, S., Wichern, G., Subramanian, A.S., Le Roux, J., "IMPROVED DOMAIN GENERALIZATION VIA DISENTANGLED MULTI-TASK LEARNING IN UNSUPERVISED ANOMALOUS SOUND DETECTION", DCASE Workshop, Lagrange, M. and Mesaros, A. and Pellegrini, T. and Richard, G. and Serizel, R. and Stowell, D., Eds., November 2022.
      BibTeX TR2022-146 PDF
      • @inproceedings{Venkatesh2022nov,
      • author = {Venkatesh, Satvik and Wichern, Gordon and Subramanian, Aswin Shanmugam and Le Roux, Jonathan},
      • title = {IMPROVED DOMAIN GENERALIZATION VIA DISENTANGLED MULTI-TASK LEARNING IN UNSUPERVISED ANOMALOUS SOUND DETECTION},
      • booktitle = {Detection and Classification of Acoustic Scenes and Events Workshop (DCASE)},
      • year = 2022,
      • editor = {Lagrange, M. and Mesaros, A. and Pellegrini, T. and Richard, G. and Serizel, R. and Stowell, D.},
      • month = nov,
      • isbn = {978-952-03-2677-7},
      • url = {https://www.merl.com/publications/TR2022-146}
      • }
  • MERL Contacts:
  • Research Areas:

    Artificial Intelligence, Speech & Audio

Abstract:

We investigate a novel multi-task learning framework that disentangles domain-shared features and domain-specific features for do- main generalization in anomalous sound detection. Disentanglement leads to better latent features and also increases flexibility in post-processing due to the availability of multiple embedding spaces. The framework was at the core of our submissions to the DCASE2022 Challenge Task 2. We ranked 5th out of 32 teams in the competition, obtaining an overall harmonic mean of 67.57% on the blind evaluation set, surpassing the baseline by 13.5% and trailing the top rank by 3.4%. We also explored machine-specific loss functions and domain generalization methods, which showed improvements on the development set, but were less effective on the evaluation set.

 

  • Related Publication

  •  Venkatesh, S., Wichern, G., Subramanian, A.S., Le Roux, J., "Disentangled surrogate task learning for improved domain generalization in unsupervised anomolous sound detection," Tech. Rep. TR2022-092, Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2022, 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}
    • }