143 citations · 197 across the 12 of their papers we have counts for
12 papers
Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis
Masahiro Yasuda, Noboru Harada, Yasunori Ohishi +3
Observations with distributed sensors are essential in analyzing a series of human and machine activities (referred to as 'events' in this paper) in complex and extensive real-worl…
6DoF SELD: Sound Event Localization and Detection Using Microphones and Motion Tracking Sensors on self-motioning human
Masahiro Yasuda, Shoichiro Saito, Akira Nakayama +1
We aim to perform sound event localization and detection (SELD) using wearable equipment for a moving human, such as a pedestrian. Conventional SELD tasks have dealt only with micr…
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…
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…
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…
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…
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…
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…
DOA Estimation by DNN-based Denoising and Dereverberation from Sound Intensity Vector
Masahiro Yasuda, Yuma Koizumi, Luca Mazzon +2
We propose a direction of arrival (DOA) estimation method that combines sound-intensity vector (IV)-based DOA estimation and DNN-based denoising and dereverberation. Since the accu…
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu +2
This paper introduces a new dataset called "ToyADMOS" designed for anomaly detection in machine operating sounds (ADMOS). To the best our knowledge, no large-scale datasets are ava…
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds
Yuma Koizumi, Shoichiro Saito, Masataka Yamaguchi +2
Use of an autoencoder (AE) as a normal model is a state-of-the-art technique for unsupervised-anomaly detection in sounds (ADS). The AE is trained to minimize the sample mean of th…
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma
Yuma Koizumi, Shoichiro Saito, Hisashi Uematsum Yuta Kawachi +1
This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The goal of unsupervised-ADS…