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pro vyhledávání: '"Yu Ha"'
Autor:
Linyi Chen, Xihong Sun, Jing Luo, Yuanshan Zhang, Yu Ha, Xiaoxia Xu, Liandi Tao, Xuefeng Mu, Shengnan Gao, Yongchao Han, Chi Wang, Fuliang Wang, Juan Wang, Bingying Yang, Xiaoyan Guo, Yajie Yu, Xian Ma, Lijian Liu, Wenmin Ma, Pengmin Xie, Chao Wang, Guoxing Li, Qingbin Lu, Fuqiang Cui
Publikováno v:
Vaccines, Vol 11, Iss 5, p 976 (2023)
(1) Background: To explore the influencing factors of human papillomavirus (HPV) vaccination among mothers and daughters so as to provide evidence and strategies for improving the HPV vaccination rate of 9–18-years-old girls. (2) A questionnaire su
Externí odkaz:
https://doaj.org/article/3ba43bbe610c41f18a6dfc0e85523d97
Autor:
Kim, Seung-bin, Lim, Chan-yeong, Heo, Jungwoo, Kim, Ju-ho, Shin, Hyun-seo, Koo, Kyo-Won, Yu, Ha-Jin
In speaker verification systems, the utilization of short utterances presents a persistent challenge, leading to performance degradation primarily due to insufficient phonetic information to characterize the speakers. To overcome this obstacle, we pr
Externí odkaz:
http://arxiv.org/abs/2406.07103
Autor:
Han, Young Joo, Yu, Ha-Jin
Modeling and synthesizing real sRGB noise is crucial for various low-level vision tasks, such as building datasets for training image denoising systems. The distribution of real sRGB noise is highly complex and affected by a multitude of factors, mak
Externí odkaz:
http://arxiv.org/abs/2312.10112
Audio deepfake detection (ADD) is the task of detecting spoofing attacks generated by text-to-speech or voice conversion systems. Spoofing evidence, which helps to distinguish between spoofed and bona-fide utterances, might exist either locally or gl
Externí odkaz:
http://arxiv.org/abs/2309.08208
Background noise considerably reduces the accuracy and reliability of speaker verification (SV) systems. These challenges can be addressed using a speech enhancement system as a front-end module. Recently, diffusion probabilistic models (DPMs) have e
Externí odkaz:
http://arxiv.org/abs/2309.08320
Background noise reduces speech intelligibility and quality, making speaker verification (SV) in noisy environments a challenging task. To improve the noise robustness of SV systems, additive noise data augmentation method has been commonly used. In
Externí odkaz:
http://arxiv.org/abs/2307.10628
The application of speech self-supervised learning (SSL) models has achieved remarkable performance in speaker verification (SV). However, there is a computational cost hurdle in employing them, which makes development and deployment difficult. Sever
Externí odkaz:
http://arxiv.org/abs/2305.17394
Autor:
Han, Young-Joo, Yu, Ha-Jin
Recently, numerous studies have been conducted on supervised learning-based image denoising methods. However, these methods rely on large-scale noisy-clean image pairs, which are difficult to obtain in practice. Denoising methods with self-supervised
Externí odkaz:
http://arxiv.org/abs/2305.09890
In music, short-term features such as pitch and tempo constitute long-term semantic features such as melody and narrative. A music genre classification (MGC) system should be able to analyze these features. In this research, we propose a novel framew
Externí odkaz:
http://arxiv.org/abs/2211.01599
The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of building task-specific models for target tasks. In the field of audio research, task-agnostic pre-trained models with high transferability and adaptability h
Externí odkaz:
http://arxiv.org/abs/2211.02227