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pro vyhledávání: '"Park, Jeongsoo"'
Autor:
Kim, Yunho, Lee, Jeong Hyun, Lee, Choongin, Mun, Juhyeok, Youm, Donghoon, Park, Jeongsoo, Hwangbo, Jemin
For reliable autonomous robot navigation in urban settings, the robot must have the ability to identify semantically traversable terrains in the image based on the semantic understanding of the scene. This reasoning ability is based on semantic trave
Externí odkaz:
http://arxiv.org/abs/2406.02989
This paper presents a novel deep learning approach for analyzing massive underwater acoustic data by leveraging a model trained on a broad spectrum of non-underwater (aerial) sounds. Recognizing the challenge in labeling vast amounts of underwater da
Externí odkaz:
http://arxiv.org/abs/2309.03451
Autor:
Park, Jeongsoo, Johnson, Justin
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 22334-22346
Most neural networks for computer vision are designed to infer using RGB images. However, these RGB images are commonly encoded in JPEG before saving to disk; decoding them imposes an unavoidable overhead for RGB networks. Instead, our work focuses o
Externí odkaz:
http://arxiv.org/abs/2211.16421
Autor:
Jeong, Il-Young, Park, Jeongsoo
This paper describes a pipeline for collecting acoustic scene data by using crowdsourcing. The detailed process of crowdsourcing is explained, including planning, validation criteria, and actual user interfaces. As a result of data collection, we pre
Externí odkaz:
http://arxiv.org/abs/2211.02289
Autor:
Park, Jeongsoo1 (AUTHOR), Kym, Dohern1,2 (AUTHOR) dohern@hallym.or.kr, Hur, Jun1,2 (AUTHOR) hammerj@hallym.or.kr, Yoon, Jaechul1 (AUTHOR), Kim, Myongjin1 (AUTHOR), Cho, Yong Suk1,2 (AUTHOR), Chun, Wook1,2 (AUTHOR), Yoon, Dogeon2 (AUTHOR)
Publikováno v:
Scientific Reports. 6/5/2024, Vol. 14 Issue 1, p1-10. 10p.
Publikováno v:
In Ceramics International May 2024
Autor:
Chang, Sungkyun, Lee, Donmoon, Park, Jeongsoo, Lim, Hyungui, Lee, Kyogu, Ko, Karam, Han, Yoonchang
Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fa
Externí odkaz:
http://arxiv.org/abs/2010.11910
In this paper, we propose a novel method that exploits music listening log data for general-purpose music feature extraction. Despite the wealth of information available in the log data of user-item interactions, it has been mostly used for collabora
Externí odkaz:
http://arxiv.org/abs/1903.02794
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Spectral envelope is one of the most important features that characterize the timbre of an instrument sound. However, it is difficult to use spectral information in the framework of conventional spectrogram decomposition methods. We overcome this pro
Externí odkaz:
http://arxiv.org/abs/1801.04081