Demo Data from VLDB 2026 / reviewers in the wild / expert
Jae-Hyoun Yoo
dblp:346/6091
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Audio and music processing
sound source localization |
0.6 | 1 | 2022 | 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 |
0.6 | 1 | 2022 | 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 |
0.2 | 1 | 2022 | 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 |
0.2 | 1 | 2022 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Multiarray Eigenbeam-ESPRIT for 3D Sound Source Localization With Multiple Spherical Microphone ArraysabstractA 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 |