Zobrazeno 1 - 10
of 355
pro vyhledávání: '"Yasuda, Masahiro"'
Autor:
Niizumi, Daisuke, Takeuchi, Daiki, Ohishi, Yasunori, Harada, Noboru, Yasuda, Masahiro, Tsubaki, Shunsuke, Imoto, Keisuke
Contrastive language-audio pre-training (CLAP) enables zero-shot (ZS) inference of audio and exhibits promising performance in several classification tasks. However, conventional audio representations are still crucial for many tasks where ZS is not
Externí odkaz:
http://arxiv.org/abs/2406.02032
Autor:
Yasuda, Masahiro, Harada, Noboru, Ohishi, Yasunori, Saito, Shoichiro, Nakayama, Akira, Ono, Nobutaka
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-world environments. This is because the information obtained from a single
Externí odkaz:
http://arxiv.org/abs/2404.08264
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 microphone arrays located in static positions. However, self-motion with t
Externí odkaz:
http://arxiv.org/abs/2403.01670
This paper provides a baseline system for First-shot-compliant unsupervised anomaly detection (ASD) for machine condition monitoring. First-shot ASD does not allow systems to do machine-type dependent hyperparameter tuning or tool ensembling based on
Externí odkaz:
http://arxiv.org/abs/2303.00455
Publikováno v:
In The Veterinary Journal October 2024 307
Autor:
Okano, Mai, Yasuda, Masahiro, Shimomura, Yui, Matsuoka, Yoshikazu, Shirouzu, Yasumasa, Fujioka, Tatsuya, Kyo, Masatoshi, Tsuji, Shoji, Kaneko, Kazunari, Hitomi, Hirofumi
Publikováno v:
In Molecular Genetics and Metabolism Reports September 2024 40
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 microphones and their weak labels. In this task, distributed sensors are util
Externí odkaz:
http://arxiv.org/abs/2202.09124
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 degraded in an unknown environment due to environmental effects such as reve
Externí odkaz:
http://arxiv.org/abs/2202.09121
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 the sound signals observed by a microphone array. Although ambisonic mi
Externí odkaz:
http://arxiv.org/abs/2202.08458
In this paper, we propose an audio declipping method that takes advantages of both sparse optimization and deep learning. Since sparsity-based audio declipping methods have been developed upon constrained optimization, they are adjustable and well-st
Externí odkaz:
http://arxiv.org/abs/2202.08028