Demo Data from VLDB 2026 / reviewers in the wild / expert
Ömer Eskidere
dblp:86/10849
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSecurity and privacy · 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.
| Network and information security
1 paper |
Biometric security · 100% | |
| Computer graphics and multimedia
1 paper |
Audio and music processing · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Biometric security
speaker recognition |
0.1 | 1 | 2012 | Recognition of Brand and Models of Cell-Phones From Recorded Speech Signals · IEEE Trans. Inf. Forensics Secur. 2012 |
Audio and music processing › audio feature extraction
mel-frequency cepstral coefficients |
0.0 | 1 | 2012 | Recognition of Brand and Models of Cell-Phones From Recorded Speech Signals · IEEE Trans. Inf. Forensics Secur. 2012 |
Audio and music processing
speech processing |
0.0 | 1 | 2012 | Recognition of Brand and Models of Cell-Phones From Recorded Speech Signals · IEEE Trans. Inf. Forensics Secur. 2012 |
Methods — techniques the papers use, named apart from their topics
vector quantization · 0.3support vector machine · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | A comparison of regression methods for remote tracking of Parkinson's disease progression
Ömer Eskidere, Figen Ertas, Cemal Hanilçi |
Expert Syst. Appl. | 1 |
| 2012 | Recognition of Brand and Models of Cell-Phones From Recorded Speech SignalsabstractSpeech signals convey various pieces of information such as the identity of its speaker, the language spoken, and the linguistic information about the text being spoken, etc. In this paper, we extract information about the cell phones from their speech records by using mel-frequency cepstrum coefficients and identify their brands and models. Closed-set identification rates of 92.56% and 96.42% have been obtained on a set of 14 different cell phones in the experiments using vector quantization and support vector machine classifiers, respectively. Cemal Hanilçi, Figen Ertas, Tuncay Ertas, Ömer Eskidere |
IEEE Trans. Inf. Forensics Secur. | 4 |