Demo Data from SIGIR 2026 / reviewers in the wild / expert
Roy Irwan
dblp:81/5789
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2002
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 · 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
acoustic signal processing |
0.0 | 1 | 2002 | Efficient tracking of the cross-correlation coefficient · IEEE Trans. Speech Audio Process. 2002 |
Audio and music processing › spatial audio
stereo audio processing |
0.0 | 1 | 2002 | Efficient tracking of the cross-correlation coefficient · IEEE Trans. Speech Audio Process. 2002 |
Methods — techniques the papers use, named apart from their topics
warping operation · 0.0recursive estimation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2002 | Efficient tracking of the cross-correlation coefficientabstractIn many (audio) processing algorithms, involving manipulation of discrete-time signals, the performance can vary strongly over the repertoire that is used. This may be the case when the signals from the various channels are allowed to be strongly positively or negatively correlated. We propose and analyze a general formula for tracking the (time-dependent) correlation between two signals. Some special cases of this formula lead to classical results known from the literature, others are new. This formula is recursive in nature, and uses only the instantaneous values of the two signals, in a low-cost and low-complexity manner; in particular, there is no need to take square roots or to carry out divisions. Furthermore, this formula can be modified with respect to the occurrence of the two signals so as to further decrease the complexity, and increase ease of implementation. The latter modification comes at the expense that not the actual correlation is tracked, but, rather, a somewhat deformed version of it. To overcome this problem, we propose, for a number of instances of the tracking formula, a simple warping operation on the deformed correlation. Now we obtain, at least for sinusoidal signals, the correct value of the correlation coefficient. Special attention is paid to the convergence behavior of the algorithm for stationary signals and the dynamic behavior if there is a transition to another stationary state; the latter is considered to be important to study the tracking abilities to nonstationary signals. We illustrate tracking algorithm by using it for stereo music fragments, obtained from a number of digital audio recordings. Ronald M. Aarts, Roy Irwan, Augustus J. E. M. Janssen |
IEEE Trans. Speech Audio Process. | 2 |