Zobrazeno 1 - 10
of 361
pro vyhledávání: '"Joon-Hyuk Chang"'
Autor:
Chee‐Hyun Park, Joon‐Hyuk Chang
Publikováno v:
IET Radar, Sonar & Navigation, Vol 18, Iss 6, Pp 825-837 (2024)
Abstract Robust localisation techniques that utilise distance observations to determine the location are focused upon. In urban environments with limited visibility and high population density, the presence of non‐line‐of‐sight signals can intr
Externí odkaz:
https://doaj.org/article/16275c670d9249b5a87a7a48efb58a34
Autor:
Yungyeo Kim, Joon-Hyuk Chang
Publikováno v:
IEEE Access, Vol 12, Pp 71606-71616 (2024)
Target sound separation (TSS) aims to separate specific sounds of interest, like a speech or a musical instrument, from complex acoustic environments with multiple overlapping sounds. In realistic scenarios, the important sounds that we want to hear
Externí odkaz:
https://doaj.org/article/09eb5807e895480ca80c77b1006018ab
Autor:
Jehyun Kyung, Joon-Young Yang, Jeong-Hwan Choi, Joon-Hyuk Chang, Sangkon Bae, Jinwoo Choi, Younho Kim
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using finger photoplethysmogram (PPG) signals. This study presents a new BP estimation system that measures PPG signals under progressive finger press
Externí odkaz:
https://doaj.org/article/35752d82d8bb4901a2a730046fac54ea
Autor:
Da-Hee Yang, Joon-Hyuk Chang
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 35, Iss 3, Pp 202-210 (2023)
In this paper, we propose a joint training framework that efficiently combines time-domain speech enhancement (SE) with an end-to-end (E2E) automatic speech recognition (ASR) system utilizing attention-based latent features. Using the latent feature
Externí odkaz:
https://doaj.org/article/06c6fed589fc4304b641bc8cf5fb9848
Autor:
Chee-Hyun Park, Joon-Hyuk Chang
Publikováno v:
IEEE Access, Vol 11, Pp 61468-61480 (2023)
This paper presents robust localization techniques that calculate location using distance observations. In enclosed and heavily populated urban environments, the positive measurement bias introduced by a non-line-of-sight signal can have a considerab
Externí odkaz:
https://doaj.org/article/cb1bb3af1aca4b2e9150d8897acd7137
Autor:
Chee‐Hyun Park, Joon‐Hyuk Chang
Publikováno v:
Electronics Letters, Vol 58, Iss 22, Pp 850-852 (2022)
Abstract Parametric approaches are primarily used in the context of robust localization. However, the localization performance is degraded when there is a mismatch between the assumed model and the actual situation. To circumvent this problem, in thi
Externí odkaz:
https://doaj.org/article/da934ab7bb624c6a8dea83759b4b5263
Autor:
Chee-Hyun Park, Joon-Hyuk Chang
Publikováno v:
IEEE Access, Vol 10, Pp 57080-57093 (2022)
This paper presents robust positioning methods that use range measurements to estimate location parameters. The existing maximum correntropy criterion-based localization algorithm uses only the $l_{2}$ norm minimization. Therefore, the localization p
Externí odkaz:
https://doaj.org/article/4832bc3837b14256b2d2193b1e65ab61
Publikováno v:
IEEE Access, Vol 10, Pp 56031-56043 (2022)
The fully convolutional time-domain speech separation network (Conv-TasNet) has been used as a backbone model in various studies because of its structural excellence. To maximize the performance and efficiency of Conv-TasNet, we attempt to apply a ne
Externí odkaz:
https://doaj.org/article/10d2db5e8fc14a62adf46c98d715ae1d
Autor:
Chee-Hyun Park, Joon-Hyuk Chang
Publikováno v:
IEEE Access, Vol 9, Pp 4059-4071 (2021)
Robust localization methods that employ distance measurements to predict the position of an emitter are proposed in this paper. The occurrence of outliers due to the non-line-of sight (NLOS) propagation of signals can drastically degrade the localiza
Externí odkaz:
https://doaj.org/article/eb194d83f5204ca4b2a25ea5100fc6e0
Autor:
Sung-Woong Hwang, Joon-Hyuk Chang
Publikováno v:
IEEE Access, Vol 9, Pp 8954-8960 (2021)
Speech synthesis has been developed to the level of natural human-level speech synthesized through an attention-based end-to-end text-to-speech synthesis (TTS) model. However, it is difficult to generate attention when synthesizing a text longer than
Externí odkaz:
https://doaj.org/article/e5e9d71aa603485cab3e50756fab497d