Demo Data from EDBT 2026 / reviewers in the wild / expert
Yameizhen Li
dblp:351/0251
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 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 · 100% |
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
microphone array processing |
0.7 | 1 | 2023 | Rotor Noise-Aware Noise Covariance Matrix Estimation for Unmanned Aerial Vehicle Audition · IEEE ACM Trans. Audio Speech Lang. Process. 2023 |
Audio and music processing
speech enhancement |
0.7 | 1 | 2023 | Rotor Noise-Aware Noise Covariance Matrix Estimation for Unmanned Aerial Vehicle Audition · IEEE ACM Trans. Audio Speech Lang. Process. 2023 |
Audio and music processing
speech processing |
0.7 | 1 | 2023 | Rotor Noise-Aware Noise Covariance Matrix Estimation for Unmanned Aerial Vehicle Audition · IEEE ACM Trans. Audio Speech Lang. Process. 2023 |
Methods — techniques the papers use, named apart from their topics
source enhancement · 0.7rotor noise modeling · 0.7power spectral density estimation · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Rotor Noise-Aware Noise Covariance Matrix Estimation for Unmanned Aerial Vehicle AuditionabstractA noise covariance matrix (NCM) estimation method for unmanned aerial vehicle (UAV) audition is proposed with rotor noise reduction as its primary focus. The proposed NCM estimation method could be incorporated into audio processing algorithms using UAV-mounted microphone array systems. The NCM is formed through accurate estimation of the microphone array input signal's amplitude and phase by using a multi-sensory rotor noise power spectral density (PSD) estimator and a filter formed by exploiting the acoustical relationship between the microphone array and the rotor noise sources, respectively. The estimated NCM aims to be readily incorporable into several source enhancement algorithms to reduce the effects of rotor noise and improve the resultant audio quality. Experiment evaluation using real in-flight UAV rotor noise recordings shows that the estimated NCM significantly improves rotor noise reduction (of up to$\sim 28$dB) and the quality of the target sound. Benjamin Yen 0001, Yameizhen Li, Yusuke Hioka |
IEEE ACM Trans. Audio Speech Lang. Process. | 2 |