activity
20182022
most citedToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions

25 citations · 33 across the 8 of their papers we have counts for

collaborators

10 papers

eess.AS2022

Multi-view and Multi-modal Event Detection Utilizing Transformer-based Multi-sensor fusion

Masahiro Yasuda, Yasunori Ohishi, Shoichiro Saito +1

We tackle a challenging task: multi-view and multi-modal event detection that detects events in a wide-range real environment by utilizing data from distributed cameras and microph…

eess.AS2022

Echo-aware Adaptation of Sound Event Localization and Detection in Unknown Environments

Masahiro Yasuda, Yasunori Ohishi, Shoichiro Saito

Our goal is to develop a sound event localization and detection (SELD) system that works robustly in unknown environments. A SELD system trained on known environment data is degrad…

eess.AS2022

Wearable SELD dataset: Dataset for sound event localization and detection using wearable devices around head

Kento Nagatomo, Masahiro Yasuda, Kohei Yatabe +2

Sound event localization and detection (SELD) is a combined task of identifying the sound event and its direction. Deep neural networks (DNNs) are utilized to associate them with t…

eess.AS202125 cited

ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions

Noboru Harada, Daisuke Niizumi, Daiki Takeuchi +3

This paper proposes a new large-scale dataset called "ToyADMOS2" for anomaly detection in machine operating sounds (ADMOS). As did for our previous ToyADMOS dataset, we collected a…

eess.AS20202 cited

A Transformer-based Audio Captioning Model with Keyword Estimation

Yuma Koizumi, Ryo Masumura, Kyosuke Nishida +2

One of the problems with automated audio captioning (AAC) is the indeterminacy in word selection corresponding to the audio event/scene. Since one acoustic event/scene can be descr…

eess.AS20202 cited

Sound Event Localization based on Sound Intensity Vector Refined By DNN-Based Denoising and Source Separation

Masahiro Yasuda, Yuma Koizumi, Shoichiro Saito +2

We propose a direction-of-arrival (DOA) estimation method for Sound Event Localization and Detection (SELD). Direct estimation of DOA using a deep neural network (DNN), i.e. comple…