Demo Data from SIGIR 2026 / reviewers in the wild / expert
Tomas Dekens
dblp:00/8161
· DBLP profile ↗
5ranked-venue papers
4as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4 · 4 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 2 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.
| Artificial intelligence
1 paper |
Speech recognition and synthesis · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Speech recognition and synthesis
speech enhancement |
0.2 | 1 | 2013 | Body Conducted Speech Enhancement by Equalization and Signal Fusion · IEEE ACM Trans. Audio Speech Lang. Process. 2013 |
Natural language and speech › Speech recognition and synthesis › noise robustness
noise robust speech processing |
0.0 | 1 | 2013 | Body Conducted Speech Enhancement by Equalization and Signal Fusion · IEEE ACM Trans. Audio Speech Lang. Process. 2013 |
Methods — techniques the papers use, named apart from their topics
signal fusion · 0.2equalization · 0.2amplitude and phase estimation · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Body Conducted Speech Enhancement by Equalization and Signal FusionabstractThis paper studies body-conducted speech for noise robust speech processing purposes. As body-conducted speech is typically limited in bandwidth, signal processing is required to obtain a signal that is both high in quality and low in noise. We propose an algorithm that first equalizes the body-conducted speech using filters obtained from a pre-defined filter set and subsequently fuses this equalized signal with a noisy conventional microphone signal using an optimal clean speech amplitude and phase estimator. We evaluated the proposed equalization and fusion technique using a combination of a conventional close-talk and a throat microphone. Subjective listening tests show that the proposed method successfully fuses the speech quality of the conventional signal and the noise robustness of the throat microphone signal. The listening tests also indicate that the inclusion of the body-conducted signal can improve single-channel speech enhancement methods, while a calculated set of objective signal quality measures confirm these observations. Tomas Dekens, Werner Verhelst |
IEEE ACM Trans. Audio Speech Lang. Process. | 1 |
| 2011 | On Noise Robust Voice Activity Detection
Tomas Dekens, Werner Verhelst |
INTERSPEECH | 1 |
| 2010 | News story segmentation in multiple modalities
Gert-Jan Poulisse, Marie-Francine Moens, Tomas Dekens, Koen Deschacht |
Multim. Tools Appl. | 3 |
| 2008 | A Multi-sensor Speech Database with Applications towards Robust Speech Processing in hostile Environments
Tomas Dekens, Yorgos Patsis, Werner Verhelst, Frédéric Beaugendre, François Capman |
LREC | 1 |
| 2007 | A comparative study of speech rate estimation techniques
Tomas Dekens, Mike Demol, Werner Verhelst, Piet Verhoeve |
INTERSPEECH | 1 |