Ms. Xingyue Cui, Nanjing Tech University, China
Dr. Xingyue Cui is a researcher and lecturer at Nanjing Tech University, China, specializing in speech processing and microphone array signal processing. He earned his Ph.D. in Information and Communication Engineering from Dalian University of Technology in 2022. Dr. Cui is an active member of the China Computer Federation (CCF) and has made significant contributions to the field of acoustic signal processing. His research focuses on sound source localization, multi-channel speech enhancement, and distributed beamforming. He has authored multiple high-impact journal articles, including publications in Applied Acoustics, IEEE Sensors Journal, and Electronics Letters, where he explores innovative methods such as RepMobileViT models for sound localization and transformer-based fault diagnosis. Through his work, Dr. Cui continues to advance speech enhancement technologies, contributing to machine learning-driven acoustic signal processing and intelligent audio systems
Professional Profile:
ORCID
Summary of Suitability for Best Researcher Award – Xingyue Cui
Xingyue Cui is a highly suitable candidate for the Best Researcher Award due to his remarkable contributions in the fields of speech processing and microphone array signal processing. His strong academic background, impactful research, and international recognition highlight his excellence in the domain of acoustic signal processing.
🎓 Education
👨🏫 Work Experience
🏆 Achievements & Contributions
✅ Research Expertise:
✅ Membership & Professional Service:
🏅 Awards & Honors
🏅 Recognized Researcher in Speech Processing & Acoustics
🏅 Active Contributor to IEEE & Applied Acoustics Journals
🏅 Innovator in Multi-Channel Speech Enhancement & Source Localization
Publication Top Notes:
Sound source localization based on three-element microphone array and RepMobileViT model
Distributed MVDR Beamformer Based on Distributed Steering Vector Estimation in Acoustic Sensor Network
Multichannel Vibration Signal Fusion Based on Rolling Bearings and MRST-Transformer Fault Diagnosis Model
Correntropy-Based Multi-objective Multi-channel Speech Enhancement
Multi-objective based multi-channel speech enhancement with BiLSTM network
Speech enhancement based on simple recurrent unit network