1 code implementation • 13 May 2023 • Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Yohei Kawaguchi
In 2023 Task 2, we focus on solving the first-shot problem, which is the challenge of training a model on a completely novel machine type.
no code implementations • 5 Apr 2023 • Tomoya Nishida, Takashi Endo, Yohei Kawaguchi
To solve this problem, we propose a domain adaptation method for SSAD where no anomalous data are available for the target domain.
2 code implementations • 13 Jun 2022 • Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi
We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques''.
no code implementations • 11 Jun 2022 • Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi
have limited representation capabilities in the latent space and, hence, poor anomaly detection performance.
2 code implementations • 27 May 2022 • Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, Yohei Kawaguchi
We present a machine sound dataset to benchmark domain generalization techniques for anomalous sound detection (ASD).
no code implementations • 15 Apr 2022 • Tomoya Nishida, Kota Dohi, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi
We have developed an unsupervised anomalous sound detection method for machine condition monitoring that utilizes an auxiliary task -- detecting when the target machine is active.
no code implementations • 12 Nov 2021 • Kota Dohi, Takashi Endo, Yohei Kawaguchi
To solve this problem, the proposed method constrains some latent variables of a normalizing flows (NF) model to represent physical parameters, which enables disentanglement of the factors of domain shifts and learning of a latent space that is invariant with respect to these domain shifts.
4 code implementations • 8 Jun 2021 • Yohei Kawaguchi, Keisuke Imoto, Yuma Koizumi, Noboru Harada, Daisuke Niizumi, Kota Dohi, Ryo Tanabe, Harsh Purohit, Takashi Endo
In 2020, we organized an unsupervised anomalous sound detection (ASD) task, identifying whether a given sound was normal or anomalous without anomalous training data.
5 code implementations • 6 May 2021 • Ryo Tanabe, Harsh Purohit, Kota Dohi, Takashi Endo, Yuki Nikaido, Toshiki Nakamura, Yohei Kawaguchi
In this paper, we introduce MIMII DUE, a new dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions.
no code implementations • 16 Mar 2021 • Kota Dohi, Takashi Endo, Harsh Purohit, Ryo Tanabe, Yohei Kawaguchi
To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed.
no code implementations • 25 Sep 2020 • Harsh Purohit, Ryo Tanabe, Takashi Endo, Kaori Suefusa, Yuki Nikaido, Yohei Kawaguchi
Failures or breakdowns in factory machinery can be costly to companies, so there is an increasing demand for automatic machine inspection.
3 code implementations • 10 Jun 2020 • Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto, Toshiki Nakamura, Yuki Nikaido, Ryo Tanabe, Harsh Purohit, Kaori Suefusa, Takashi Endo, Masahiro Yasuda, Noboru Harada
The main challenge of this task is to detect unknown anomalous sounds under the condition that only normal sound samples have been provided as training data.
1 code implementation • 19 May 2020 • Kaori Suefusa, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Yohei Kawaguchi
However, when the target machine sound is non-stationary, a reconstruction error tends to be large independent of an anomaly, and its variations increased because of the difficulty of predicting the edge frames.
5 code implementations • 20 Sep 2019 • Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, Yohei Kawaguchi
The purpose of releasing the MIMII dataset is to assist the machine-learning and signal-processing community with their development of automated facility maintenance.