Demo Data from EDBT 2026 / reviewers in the wild / expert
Christopher Jacoby
dblp:142/8787
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
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 · 50% Multimedia analysis and retrieval · 50% | |
| Databases, data mining, and information retrieval
1 paper |
Machine learning and data management · 100% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Audio and music processing › audio classification
environmental sound classification |
0.2 | 1 | 2014 | A Dataset and Taxonomy for Urban Sound Research · ACM Multimedia 2014 |
Methods — techniques the papers use, named apart from their topics
baseline audio classification · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | A Dataset and Taxonomy for Urban Sound ResearchabstractAutomatic urban sound classification is a growing area of research with applications in multimedia retrieval and urban informatics. In this paper we identify two main barriers to research in this area - the lack of a common taxonomy and the scarceness of large, real-world, annotated data. To address these issues we present a taxonomy of urban sounds and a new dataset, UrbanSound, containing 27 hours of audio with 18.5 hours of annotated sound event occurrences across 10 sound classes. The challenges presented by the new dataset are studied through a series of experiments using a baseline classification system. Justin Salamon, Christopher Jacoby, Juan Pablo Bello |
ACM Multimedia | 2 |