no code implementations • 29 Oct 2024 • Tomoya Nishida, Harsh Purohit, Kota Dohi, Takashi Endo, Yohei Kawaguchi
This paper proposes a framework of explaining anomalous machine sounds in the context of anomalous sound detection~(ASD).
no code implementations • 29 Oct 2024 • Ryoya Ogura, Tomoya Nishida, Yohei Kawaguchi
The proposed method employs a retrieval-augmented approach for captioning of anomalous sounds.
no code implementations • 12 Oct 2024 • Ryotaro Nagase, Takashi Sumiyoshi, Natsuo Yamashita, Kota Dohi, Yohei Kawaguchi
We also focus on purchase intention as a bipolar emotion and investigate the model's performance to zero-shot estimate it.
no code implementations • 27 Sep 2024 • Harsh Purohit, Tomoya Nishida, Kota Dohi, Takashi Endo, Yohei Kawaguchi
Insufficient recordings and the scarcity of anomalies present significant challenges in developing and validating robust anomaly detection systems for machine sounds.
no code implementations • 25 Sep 2024 • Kota Dohi, Aoi Ito, Harsh Purohit, Tomoya Nishida, Takashi Endo, Yohei Kawaguchi
Due to scarcity of time-series data annotated with descriptive texts, training a model to generate descriptive texts for time-series data is challenging.
1 code implementation • 11 Jun 2024 • Tomoya Nishida, Noboru Harada, Daisuke Niizumi, Davide Albertini, Roberto Sannino, Simone Pradolini, Filippo Augusti, Keisuke Imoto, Kota Dohi, Harsh Purohit, Takashi Endo, Yohei Kawaguchi
We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024 Challenge Task 2: First-shot unsupervised anomalous sound detection (ASD) for machine condition monitoring.
no code implementations • 25 Mar 2024 • Kota Dohi, Yohei Kawaguchi
To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed.
no code implementations • 2 Sep 2023 • Shota Horiguchi, Kota Dohi, Yohei Kawaguchi
One of the challenges in deploying a machine learning model is that the model's performance degrades as the operating environment changes.
2 code implementations • 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.
no code implementations • 1 Jul 2022 • Yuki Takashima, Shota Horiguchi, Shinji Watanabe, Paola García, Yohei Kawaguchi
In this paper, we present an incremental domain adaptation technique to prevent catastrophic forgetting for an end-to-end automatic speech recognition (ASR) model.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
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.
no code implementations • 6 Jun 2022 • Shota Horiguchi, Shinji Watanabe, Paola Garcia, Yuki Takashima, Yohei Kawaguchi
Finally, to improve online diarization, our method improves the buffer update method and revisits the variable chunk-size training of EEND.
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 • 1 Dec 2021 • Yuki Okamoto, Shota Horiguchi, Masaaki Yamamoto, Keisuke Imoto, Yohei Kawaguchi
An onomatopoeic word, which is a character sequence that phonetically imitates a sound, is effective in expressing characteristics of sound such as duration, pitch, and timbre.
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.
no code implementations • 10 Oct 2021 • Shota Horiguchi, Yuki Takashima, Paola Garcia, Shinji Watanabe, Yohei Kawaguchi
With simulated and real-recorded datasets, we demonstrated that the proposed method outperformed conventional EEND when a multi-channel input was given while maintaining comparable performance with a single-channel input.
no code implementations • 4 Jul 2021 • Shota Horiguchi, Shinji Watanabe, Paola Garcia, Yawen Xue, Yuki Takashima, Yohei Kawaguchi
This makes it possible to produce diarization results of a large number of speakers for the whole recording even if the number of output speakers for each subsequence is limited.
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.