Zobrazeno 1 - 10
of 2 260
pro vyhledávání: '"Yu-in Jin"'
Autor:
Wang, Zhao, Li, Ji-Xia, Zhang, Ke, Wu, 1 Feng-Quan, Tian, Hai-Jun, Niu, Chen-Hui, Zhang, Ju-Yong, Chen, Zhi-Ping, Yu, Dong-Jin, Chen, Xue-Lei
The digital correlator is one of the most crucial data processing components of a radio telescope array. With the scale of radio interferometeric array growing, many efforts have been devoted to developing a cost-effective and scalable correlator in
Externí odkaz:
http://arxiv.org/abs/2406.19013
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