105 citations · 292 across the 35 of their papers we have counts for
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Environmental Sound Extraction Using Onomatopoeic Words
Yuki Okamoto, Shota Horiguchi, Masaaki Yamamoto +2
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. We…
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…
MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions
Ryo Tanabe, Harsh Purohit, Kota Dohi +4
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 environ…
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…