34 citations · 106 across the 30 of their papers we have counts for
31 papers
Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models
Koki Takeshita, Kenji Uebayashi, Hikaru Tomonari +6
Recent studies have proposed reinforcement learning with verifiable rewards (RLVR) methods that enable LLMs to generate high-quality RTL circuits from natural-language specificatio…
Bridging the Sim-to-Real Gap in Semiconductor Visual Program Synthesis via Input Binarization
Yusuke Ohtsubo, Kota Dohi, Koichiro Yawata +2
Precise parametric control over circuit geometry is essential for semiconductor inspection, yet obtaining sufficient real training data remains costly. Although generative models s…
Description and Discussion on DCASE 2026 Challenge Task 2: Noise-aware Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Tomoya Nishida, Noboru Harada, Daiki Takeuchi +6
This paper presents an overview of DCASE 2026 Challenge Task 2, titled "Noise-aware unsupervised anomalous sound detection (UASD) for machine condition monitoring." The task aims t…
Synthetic Data Domain Adaptation for ASR via LLM-based Text and Phonetic Respelling Augmentation
Natsuo Yamashita, Koichi Nagatsuka, Hiroaki Kokubo +2
End-to-end automatic speech recognition often degrades on domain-specific data due to scarce in-domain resources. We propose a synthetic-data-based domain adaptation framework with…
LaSTR: Language-Driven Time-Series Segment Retrieval
Kota Dohi, Harsh Purohit, Tomoya Nishida +6
Effectively searching time-series data is essential for system analysis, but existing methods often require expert-designed similarity criteria or rely on global, series-level desc…
Retaining Mixture Representations for Domain Generalized Anomalous Sound Detection
Phurich Saengthong, Tomoya Nishida, Kota Dohi +2
Anomalous sound detection (ASD) in the wild requires robustness to distribution shifts such as unseen low-SNR input mixtures of machine and noise types. State-of-the-art systems ex…
Can VLM Pseudo-Labels Train a Time-Series QA Model That Outperforms the VLM?
Takuya Fujimura, Kota Dohi, Natsuo Yamashita +1
Time-series question answering (TSQA) tasks face significant challenges due to the lack of labeled data. Alternatively, with recent advancements in large-scale models, vision-langu…
DiffNator: Generating Structured Explanations of Time-Series Differences
Kota Dohi, Tomoya Nishida, Harsh Purohit +2
In many IoT applications, the central interest lies not in individual sensor signals but in their differences, yet interpreting such differences requires expert knowledge. We propo…
Automatic Inspection Based on Switch Sounds of Electric Point Machines
Ayano Shibata, Toshiki Gunji, Mitsuaki Tsuda +4
Since 2018, East Japan Railway Company and Hitachi, Ltd. have been working to replace human inspections with IoT-based monitoring. The purpose is Labor-saving required for equipmen…
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…
Description and Discussion on DCASE 2025 Challenge Task 2: First-shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Tomoya Nishida, Noboru Harada, Daisuke Niizumi +9
This paper introduces the task description for the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge Task 2, titled "First-shot unsupervised anomalo…
Retrieving Time-Series Differences Using Natural Language Queries
Kota Dohi, Tomoya Nishida, Harsh Purohit +2
Effectively searching time-series data is essential for system analysis; however, traditional methods often require domain expertise to define search criteria. Recent advancements…
CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision
Aoi Ito, Kota Dohi, Yohei Kawaguchi
This paper presents CLaSP, a novel model for retrieving time-series signals using natural language queries that describe signal characteristics. The ability to search time-series s…
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…
Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data
Kota Dohi, Aoi Ito, Harsh Purohit +3
Due to scarcity of time-series data annotated with descriptive texts, training a model to generate descriptive texts for time-series data is challenging. In this study, we propose…
Stream-based Active Learning for Anomalous Sound Detection in Machine Condition Monitoring
Tuan Vu Ho, Kota Dohi, Yohei Kawaguchi
This paper introduces an active learning (AL) framework for anomalous sound detection (ASD) in machine condition monitoring system. Typically, ASD models are trained solely on norm…
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…
Streaming Active Learning for Regression Problems Using Regression via Classification
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. To maintain the performance, streaming ac…
CAPTDURE: Captioned Sound Dataset of Single Sources
Yuki Okamoto, Kanta Shimonishi, Keisuke Imoto +3
In conventional studies on environmental sound separation and synthesis using captions, datasets consisting of multiple-source sounds with their captions were used for model traini…
Anomalous Sound Detection Based on Sound Separation
Kanta Shimonishi, Kota Dohi, Yohei Kawaguchi
This paper proposes an unsupervised anomalous sound detection method using sound separation. In factory environments, background noise and non-objective sounds obscure desired mach…
Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Kota Dohi, Keisuke Imoto, Noboru Harada +7
We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2023 Challenge Task 2: ``First-shot unsupervised anomalous sound detection…