Demo Data from VLDB 2026 / reviewers in the wild / expert
Branimir Dropuljic
dblp:08/7219
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2018
0000-0001-5748-2643ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author
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 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Audio and music processing
speech production |
0.3 | 1 | 2018 | Vocal Analysis of Acoustic Startle Responses · IEEE ACM Trans. Audio Speech Lang. Process. 2018 |
Audio and music processing › speech analysis
voice analysis |
0.3 | 1 | 2018 | Vocal Analysis of Acoustic Startle Responses · IEEE ACM Trans. Audio Speech Lang. Process. 2018 |
Methods — techniques the papers use, named apart from their topics
surface electromyography · 0.3linear mixed effects model · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Vocal Analysis of Acoustic Startle ResponsesabstractThis paper presents an extensive statistical analysis of the acoustic startle response of two vocal parameters: fundamental frequency (F0) and root-mean-square energy (E), as well as of the orbicularis oculi (eyeblink) surface electromyography (sEMG). An experiment was conducted in which fourteen participants were exposed to acoustic startle stimuli of varying parameters, i.e., intensity level, duration, rise time, and spectral type, during periods of sustained phonation. Voice recordings of the phonations were taken alongside several physiological signals, of which only the sEMG was analyzed in this paper. Response features (peak value, peak time, latency, rise time, fall time, and duration) were extracted on F0, E and sEMG data, and statistical analysis was conducted using linear mixed effects models to show the response behavior with the varying stimuli. The results for vocal F0 and E data were congruent with sEMG data and earlier work in the field. The results demonstrated that vocal analysis can be used as a feasible alternative to the sEMG eyeblink analysis of acoustic startle responses. Branimir Dropuljic, Igor Mijic, Davor Petrinovic, Tanja Jovanovic, Kresimir Cosic |
IEEE ACM Trans. Audio Speech Lang. Process. | 1 |
| 2017 | Evaluation of Speech Perturbation Features for Measuring Authenticity in Stress Expressions
Branimir Dropuljic, Leo Mrsic, Robert Kopal, Sandro Skansi, Andrijana Brkic |
ACIIDS (1) | 1 |