14 citations · 18 across the 12 of their papers we have counts for
12 papers
Wave Tank Experiment for Sea State Monitoring with Distributed Acoustic Sensing
Yoshiyuki Yajima, Sakiko Mishima, Noriyuki Tonami +4
Monitoring sea states across the offshore wind farm areas is essential to keep their structures safe, efficiently operate the systems, and assess the environmental effects of wind…
Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing
Sakiko Mishima, Yoshiyuki Yajima, Noriyuki Tonami +4
This study proposes an anomaly-detection framework for monitoring exposure-length variations in submarine free-span cables using Distributed Acoustic Sensing (DAS), which is one of…
Event Classification by Physics-informed Inpainting for Distributed Multichannel Acoustic Sensor with Partially Degraded Channels
Noriyuki Tonami, Wataru Kohno, Yoshiyuki Yajima +4
Distributed multichannel acoustic sensing (DMAS) enables large-scale sound event classification (SEC), but performance drops when many channels are degraded and when sensor layouts…
Trainingless Adaptation of Pretrained Models for Environmental Sound Classification
Noriyuki Tonami, Wataru Kohno, Keisuke Imoto +4
Deep neural network (DNN)-based models for environmental sound classification are not robust against a domain to which training data do not belong, that is, out-of-distribution or…
Low-rank constrained multichannel signal denoising considering channel-dependent sensitivity inspired by self-supervised learning for optical fiber sensing
Noriyuki Tonami, Wataru Kohno, Sakiko Mishima +3
Optical fiber sensing is a technology wherein audio, vibrations, and temperature are detected using an optical fiber; especially the audio/vibrations-aware sensing is called distri…
Sound Event Triage: Detecting Sound Events Considering Priority of Classes
Noriyuki Tonami, Keisuke Imoto
We propose a new task for sound event detection (SED): sound event triage (SET). The goal of SET is to detect an arbitrary number of high-priority event classes while allowing misd…
Sound Event Detection Guided by Semantic Contexts of Scenes
Noriyuki Tonami, Keisuke Imoto, Ryotaro Nagase +3
Some studies have revealed that contexts of scenes (e.g., "home," "office," and "cooking") are advantageous for sound event detection (SED). Mobile devices and sensing technologies…
Sound Event Detection Based on Curriculum Learning Considering Learning Difficulty of Events
Noriyuki Tonami, Keisuke Imoto, Yuki Okamoto +2
In conventional sound event detection (SED) models, two types of events, namely, those that are present and those that do not occur in an acoustic scene, are regarded as the same t…
Joint Analysis of Sound Events and Acoustic Scenes Using Multitask Learning
Noriyuki Tonami, Keisuke Imoto, Ryosuke Yamanishi +1
Sound event detection (SED) and acoustic scene classification (ASC) are important research topics in environmental sound analysis. Many research groups have addressed SED and ASC u…
Sound Event Detection Using Duration Robust Loss Function
Daichi Akiyama, Keisuke Imoto, Noriyuki Tonami +4
Many methods of sound event detection (SED) based on machine learning regard a segmented time frame as one data sample to model training. However, the sound durations of sound even…
Sound Event Detection by Multitask Learning of Sound Events and Scenes with Soft Scene Labels
Keisuke Imoto, Noriyuki Tonami, Yuma Koizumi +3
Sound event detection (SED) and acoustic scene classification (ASC) are major tasks in environmental sound analysis. Considering that sound events and scenes are closely related to…
Joint Analysis of Acoustic Events and Scenes Based on Multitask Learning
Noriyuki Tonami, Keisuke Imoto, Masahiro Niitsuma +2
Acoustic event detection and scene classification are major research tasks in environmental sound analysis, and many methods based on neural networks have been proposed. Convention…