Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Yuma Koizumi"'
Autor:
Yuma Koizumi, Ryo Kunimatsu, Isamu Kado, Yuki Yoshimi, Sakura Yamada, Tomohiro Ogasawara, Kotaro Tanimoto
Publikováno v:
Journal of Clinical Medicine, Vol 13, Iss 2, p 622 (2024)
This cross-sectional study aimed to explore the correlation between maxillofacial morphology and caries risk, assessed using salivary tests, in orthodontic patients. Despite enhancing the oral health-related quality of life, orthodontic treatment may
Externí odkaz:
https://doaj.org/article/4d3caef1fc5d4526b9d69373d44cfe54
Publikováno v:
Microbiology Spectrum, Vol 11, Iss 4 (2023)
ABSTRACT Bacterial hyaluronate lyases (Hys) are enzymes that degrade hyaluronic acid in their host and are known to contribute to the pathogenesis of several illnesses. The first two identified Hys genes in Staphylococcus aureus were registered as hy
Externí odkaz:
https://doaj.org/article/e82a8a6f450f459cbcae3207decfedac
Autor:
Hiroki HASE, Yuichi MINE, Shota OKAZAKI, Yuki YOSHIMI, Shota ITO, Tzu-Yu PENG, Mizuho SANO, Yuma KOIZUMI, Naoya KAKIMOTO, Kotaro TANIMOTO, Takeshi MURAYAMA
Publikováno v:
Dental Materials Journal; 2024, Vol. 43 Issue 3, p394-399, 6p
Publikováno v:
2022 IEEE Spoken Language Technology Workshop (SLT).
Denoising diffusion probabilistic models (DDPMs) and generative adversarial networks (GANs) are popular generative models for neural vocoders. The DDPMs and GANs can be characterized by the iterative denoising framework and adversarial training, resp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::806a1952bf9d1889dbb9e0babbc78004
http://arxiv.org/abs/2210.01029
http://arxiv.org/abs/2210.01029
Publikováno v:
Microbiology Resource Announcements. 11
We report the complete genome sequence of Staphylococcus aureus strain JP025, which was isolated from a furunculosis sample from a Japanese patient. The strain carried two hyaluronate lyase genes, JP025 hysA and JP025 hysB , on the chromosome and was
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing. 15:37-50
In this paper, we propose a phase reconstruction framework, named Deep Griffin–Lim Iteration (DeGLI). Phase reconstruction is a fundamental technique for improving the quality of sound obtained through some process in the time-frequency domain. It
Publikováno v:
Acoustical Science and Technology. 41:769-775
Publikováno v:
2021 29th European Signal Processing Conference (EUSIPCO).
Deep neural network (DNN)-based speech enhancement ordinarily requires clean speech signals as the training target. However, collecting clean signals is very costly because they must be recorded in a studio. This requirement currently restricts the a
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 26:1780-1792
We propose a training method for deep neural network (DNN)-based source enhancement to increase objective sound quality assessment (OSQA) scores such as the perceptual evaluation of speech quality (PESQ). In many conventional studies, DNNs have been