105 citations · 292 across the 35 of their papers we have counts for
18 papers · 1 filter
MIMII-Agent: Leveraging LLMs with Function Calling for Relative Evaluation of Anomalous Sound Detection
Harsh Purohit, Tomoya Nishida, Kota Dohi +2
This paper proposes a method for generating machine-type-specific anomalies to evaluate the relative performance of unsupervised anomalous sound detection (UASD) systems across dif…
Retrieval-Augmented Approach for Unsupervised Anomalous Sound Detection and Captioning without Model Training
Ryoya Ogura, Tomoya Nishida, Yohei Kawaguchi
This paper proposes a method for unsupervised anomalous sound detection (UASD) and captioning the reason for detection. While there is a method that captions the difference between…
Timbre Difference Capturing in Anomalous Sound Detection
Tomoya Nishida, Harsh Purohit, Kota Dohi +2
This paper proposes a framework of explaining anomalous machine sounds in the context of anomalous sound detection~(ASD). While ASD has been extensively explored, identifying how a…
Can We Estimate Purchase Intention Based on Zero-shot Speech Emotion Recognition?
Ryotaro Nagase, Takashi Sumiyoshi, Natsuo Yamashita +2
This paper proposes a zero-shot speech emotion recognition (SER) method that estimates emotions not previously defined in the SER model training. Conventional methods are limited t…
MIMII-Gen: Generative Modeling Approach for Simulated Evaluation of Anomalous Sound Detection System
Harsh Purohit, Tomoya Nishida, Kota Dohi +2
Insufficient recordings and the scarcity of anomalies present significant challenges in developing and validating robust anomaly detection systems for machine sounds. To address th…
Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Tomoya Nishida, Noboru Harada, Daisuke Niizumi +9
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 (…
Distributed collaborative anomalous sound detection by embedding sharing
Kota Dohi, Yohei Kawaguchi
To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed. In this paper, we explore a method for multiple clients to collaboratively learn a…
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models
Yuki Takashima, Shota Horiguchi, Shinji Watanabe +2
In this paper, we present an incremental domain adaptation technique to prevent catastrophic forgetting for an end-to-end automatic speech recognition (ASR) model. Conventional app…
Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors
Shota Horiguchi, Shinji Watanabe, Paola Garcia +2
A method to perform offline and online speaker diarization for an unlimited number of speakers is described in this paper. End-to-end neural diarization (EEND) has achieved overlap…
Anomalous Sound Detection Based on Machine Activity Detection
Tomoya Nishida, Kota Dohi, Takashi Endo +2
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.…
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts
Kota Dohi, Takashi Endo, Yohei Kawaguchi
To develop a sound-monitoring system for machines, a method for detecting anomalous sound under domain shifts is proposed. A domain shift occurs when a machine's physical parameter…
Multi-Channel End-to-End Neural Diarization with Distributed Microphones
Shota Horiguchi, Yuki Takashima, Paola Garcia +2
Recent progress on end-to-end neural diarization (EEND) has enabled overlap-aware speaker diarization with a single neural network. This paper proposes to enhance EEND by using mul…
Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors
Shota Horiguchi, Shinji Watanabe, Paola Garcia +3
Attractor-based end-to-end diarization is achieving comparable accuracy to the carefully tuned conventional clustering-based methods on challenging datasets. However, the main draw…
Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions
Yohei Kawaguchi, Keisuke Imoto, Yuma Koizumi +6
We present the task description and discussion on the results of the DCASE 2021 Challenge Task 2. In 2020, we organized an unsupervised anomalous sound detection (ASD) task, identi…
Flow-based Self-supervised Density Estimation for Anomalous Sound Detection
Kota Dohi, Takashi Endo, Harsh Purohit +2
To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed. Exact likelihood estimation using Normalizing Flows is a promising technique for u…
Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization
Harsh Purohit, Ryo Tanabe, Takashi Endo +3
Failures or breakdowns in factory machinery can be costly to companies, so there is an increasing demand for automatic machine inspection. Existing approaches to acoustic signal-ba…
Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto +8
In this paper, we present the task description and discuss the results of the DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for Machine Condition Monitori…
Anomalous sound detection based on interpolation deep neural network
Kaori Suefusa, Tomoya Nishida, Harsh Purohit +3
As the labor force decreases, the demand for labor-saving automatic anomalous sound detection technology that conducts maintenance of industrial equipment has grown. Conventional a…