Search Results for author: Yohei Kawaguchi

Found 27 papers, 9 papers with code

Timbre Difference Capturing in Anomalous Sound Detection

no code implementations29 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).

Can We Estimate Purchase Intention Based on Zero-shot Speech Emotion Recognition?

no code implementations12 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.

Speech Emotion Recognition

MIMII-Gen: Generative Modeling Approach for Simulated Evaluation of Anomalous Sound Detection System

no code implementations27 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.

Anomaly Detection FAD

Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data

no code implementations25 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.

Contrastive Learning Descriptive +1

Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

1 code implementation11 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.

Attribute Domain Generalization +1

Distributed collaborative anomalous sound detection by embedding sharing

no code implementations25 Mar 2024 Kota Dohi, Yohei Kawaguchi

To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed.

Federated Learning

Streaming Active Learning for Regression Problems Using Regression via Classification

no code implementations2 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.

Active Learning Classification +1

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

no code implementations5 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

Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models

no code implementations1 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

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

2 code implementations13 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

Anomalous Sound Detection Based on Machine Activity Detection

no code implementations15 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

Environmental Sound Extraction Using Onomatopoeic Words

no code implementations1 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.

Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts

no code implementations12 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.

Disentanglement Unsupervised Anomaly Detection

Multi-Channel End-to-End Neural Diarization with Distributed Microphones

no code implementations10 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.

speaker-diarization Speaker Diarization

Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors

no code implementations4 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.

Clustering

MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions

5 code implementations6 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.

Task 2

Anomalous sound detection based on interpolation deep neural network

1 code implementation19 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.

MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

5 code implementations20 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.

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