Demo Data from VLDB 2026 / reviewers in the wild / expert
Jon Fagerström
dblp:320/7218
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2023
0000-0001-7731-7805ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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 · 91% Virtual and augmented reality · 9% |
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 › audio effects
artificial reverberation |
0.7 | 1 | 2023 | Decorrelation in Feedback Delay Networks · IEEE ACM Trans. Audio Speech Lang. Process. 2023 |
Audio and music processing
decorrelation |
0.7 | 1 | 2023 | Decorrelation in Feedback Delay Networks · IEEE ACM Trans. Audio Speech Lang. Process. 2023 |
Audio and music processing › audio effects › artificial reverberation
feedback delay network |
0.7 | 1 | 2023 | Decorrelation in Feedback Delay Networks · IEEE ACM Trans. Audio Speech Lang. Process. 2023 |
Virtual and augmented reality
immersive audio |
0.2 | 1 | 2023 | Decorrelation in Feedback Delay Networks · IEEE ACM Trans. Audio Speech Lang. Process. 2023 |
Methods — techniques the papers use, named apart from their topics
feedback matrix analysis · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Decorrelation in Feedback Delay NetworksabstractThe feedback delay network (FDN) is a popular filter structure to generate artificial spatial reverberation. A common requirement for multichannel late reverberation is that the output signals are well decorrelated, as too high a correlation can lead to poor reproduction of source image and uncontrolled coloration. This article presents the analysis of multichannel correlation induced by FDNs. It is shown that the correlation depends primarily on the feedforward paths, while the long reverberation tail produced by the recursive path does not contribute to the inter-channel correlation. The impact of the feedback matrix type, size, and delays on the inter-channel correlation is demonstrated. The results show that small FDNs with a few feedback channels tend to have a high inter-channel correlation, and that the use of a filter feedback matrix significantly improves the decorrelation, often leading to the lowest inter-channel correlation among the tested cases. The learnings of this work support the practical design of multichannel artificial reverberators for immersive audio applications. Sebastian J. Schlecht, Jon Fagerström, Vesa Välimäki |
IEEE ACM Trans. Audio Speech Lang. Process. | 2 |
| 2021 | One-to-Many Conversion for Percussive SamplesabstractA filtering algorithm for generating subtle random variations in sampled sounds is proposed. Using only one recording for impact sound effects or drum machine sounds results in unrealistic repetitiveness during consecutive playback. This paper studies spectral variations in repeated knocking sounds and in three drum sounds: a hihat, a snare, and a tomtom. The proposed method uses a short pseudo-random velvet-noise filter and a low-shelf filter to produce timbral variations targeted at appropriate spectral regions, yielding potentially an endless number of new realistic versions of a single percussive sampled sound. The realism of the resulting processed sounds is studied in a listening test. The results show that the sound quality obtained with the proposed algorithm is at least as good as that of a previous method while using 77% fewer computational operations. The algorithm is widely applicable to computer-generated music and game audio. Jon Fagerström, Sebastian J. Schlecht, Vesa Välimäki |
DAFx | 1 |