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Yameizhen Li

dblp:351/0251 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Audio and music processing
microphone array processing
0.712023
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
0.712023
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
0.712023
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
YearPublicationVenuePosition
2023 Rotor Noise-Aware Noise Covariance Matrix Estimation for Unmanned Aerial Vehicle Audition
abstract
A 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