Demo Data from VLDB 2026 / reviewers in the wild / expert
Reza Ghanavi
dblp:324/4893
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0001-8078-9707ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 · 67% Virtual and augmented reality · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Audio and music processing › speech enhancement
binaural speech enhancement |
0.8 | 1 | 2024 | Adjustable Coherent-to-Diffuse Power Estimator for Binaural Speech Enhancement in Multi-Talker Environments · IEEE ACM Trans. Audio Speech Lang. Process. 2024 |
Virtual and augmented reality › auditory perception › psychoacoustics
spatial audio perception |
0.8 | 1 | 2024 | Adjustable Coherent-to-Diffuse Power Estimator for Binaural Speech Enhancement in Multi-Talker Environments · IEEE ACM Trans. Audio Speech Lang. Process. 2024 |
Audio and music processing
speech enhancement |
0.8 | 1 | 2024 | Adjustable Coherent-to-Diffuse Power Estimator for Binaural Speech Enhancement in Multi-Talker Environments · IEEE ACM Trans. Audio Speech Lang. Process. 2024 |
Methods — techniques the papers use, named apart from their topics
geometrical interpretation · 0.8complex coherence function · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Adjustable Coherent-to-Diffuse Power Estimator for Binaural Speech Enhancement in Multi-Talker EnvironmentsabstractThe binaural coherence-to-diffuse power ratio (CDR) estimate in reverberant environments is essential in many speech enhancement algorithms applied within hear-through systems. In this work, we propose a parameterised and adjustable binaural CDR estimator whose formulation is based on a geometrical interpretation of the short-time complex coherence function between binaural microphone signals. Conventional CDR estimators often distort the natural spectro-temporal behaviour of the noise field by relying on theoretical coherence models of the desired signal and/or diffuse noise field. Our proposed CDR estimator relies only on the observed spatial coherence and better preserves the natural characteristics of a binaural noise field. We demonstrate that the proposed CDR estimator can be used effectively for binaural dereverberation and denoising of broadside speech in multi-talker and noisy acoustic conditions and that it often outperforms state-of-the-art coherence-based methods for dereverberation and denoising. Furthermore, the adjustable parameter enables one to minimise the frequency-dependent estimation error of the binaural system in different environments. Reza Ghanavi, Craig T. Jin |
IEEE ACM Trans. Audio Speech Lang. Process. | 1 |