Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

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.

Task 2

Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection

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.

Decoder Domain Adaptation +2

Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques

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''.

domain classification Domain Generalization +1

MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task

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).

Benchmarking Domain Generalization

Anomalous Sound Detection Based on Machine Activity Detection

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.

Action Detection Activity Detection +2

MeshRIR: A Dataset of Room Impulse Responses on Meshed Grid Points For Evaluating Sound Field Analysis and Synthesis Methods

2 code implementations • 21 Jun 2021 • Shoichi Koyama, Tomoya Nishida, Keisuke Kimura, Takumi Abe, Natsuki Ueno, Jesper Brunnström

Two subdatasets are currently available: one consists of IRs in a three-dimensional cuboidal region from a single source, and the other consists of IRs in a two-dimensional square region from an array of 32 sources.

Distant Speech Recognition Room Impulse Response (RIR) +2

Anomalous sound detection based on interpolation deep neural network

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.