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Jae-Hyoun Yoo

dblp:346/6091 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-9326-9353ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Audio and music processing · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing
sound source localization
0.612022
Multiarray Eigenbeam-ESPRIT for 3D Sound Source Localization With Multiple Spherical Microphone Arrays · IEEE ACM Trans. Audio Speech Lang. Process. 2022
Audio and music processing › microphone array processing
spherical microphone array processing
0.612022
Multiarray Eigenbeam-ESPRIT for 3D Sound Source Localization With Multiple Spherical Microphone Arrays · IEEE ACM Trans. Audio Speech Lang. Process. 2022
Audio and music processing › spatial audio
binaural reproduction
0.212022
Multiarray Eigenbeam-ESPRIT for 3D Sound Source Localization With Multiple Spherical Microphone Arrays · IEEE ACM Trans. Audio Speech Lang. Process. 2022
Audio and music processing
spatial audio
0.212022
Multiarray Eigenbeam-ESPRIT for 3D Sound Source Localization With Multiple Spherical Microphone Arrays · IEEE ACM Trans. Audio Speech Lang. Process. 2022

Methods — techniques the papers use, named apart from their topics

schur decomposition · 0.6eigenbeam processing · 0.6ESPRIT · 0.6
YearPublicationVenuePosition
2022 Multiarray Eigenbeam-ESPRIT for 3D Sound Source Localization With Multiple Spherical Microphone Arrays
abstract
A 3D sound source localization (SSL) technique for multiple spherical arrays is proposed. Spherical arrays have been popularly used for direction of arrival (DoA) estimation, which serves as important prior knowledge for source separation, dereverberation, and binaural rendering for VR applications. In this work, a parametric 3D SSL technique is proposed that can detect multiple sources using multiple spherical array recordings. From the enriched information by using multiple arrays, we show that the parametric estimation of 3D positions is possible without scanning candidate positions in 3D space. To mitigate the problem of the ambiguous association between DoAs estimated from different arrays, a total covariance matrix including both auto- and cross-covariance matrices between arrays is utilized such that DoAs from arrays are automatically paired to uniquely localize 3D source positions. The joint parameter estimation is based on the generalized joint Schur decomposition of multiple matrices combined with a geometric projection robustly guiding the convergence of the proposed iteration algorithm. Simulations conducted in anechoic and reverberant room conditions reveal that the proposed technique can accurately determine multiple source positions in 3D space without ambiguities.
Jung-Woo Choi, Franz Zotter, Byeongho Jo, Jae-Hyoun Yoo
IEEE ACM Trans. Audio Speech Lang. Process.4