World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
8191
World Ranking
10062
National Ranking
4242

Overview

Lie Lu is affiliated with Dolby in the United States and has contributed to research primarily in the field of Computer Science. Their work spans several subfields including Signal Processing, Computer Networks and Communications, Computational Mechanics, Artificial Intelligence, and Computer Vision and Pattern Recognition.

The scientist's research topics focus mainly on Speech and Audio Processing, Music and Audio Processing, and Advanced Adaptive Filtering Techniques. Additional areas of interest include Cell Image Analysis Techniques, Speech Recognition and Synthesis, Software-Defined Networks and 5G, and Network Traffic and Congestion Control.

Lie Lu has published extensively in multiple venues, with frequent appearances in:

  • arXiv (Cornell University)
  • 2021 IEEE Global Communications Conference (GLOBECOM)
  • Journal of the Audio Engineering Society
  • IEEE Journal of Selected Topics in Signal Processing
  • Proceedings of the AAAI Conference on Artificial Intelligence

Notable recent papers include:

  • EPC-TE: Explicit Path Control in Traffic Engineering with Deep Reinforcement Learning, 2021, 2021 IEEE Global Communications Conference (GLOBECOM)
  • CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer Learning, 2024, arXiv (Cornell University)
  • High Quality Audio Coding with MDCTNet, 2022, arXiv (Cornell University)
  • Stereo Speech Enhancement Using Custom Mid-Side Signals and Monaural Processing, 2022, arXiv (Cornell University)
  • DeepSpace: Dynamic Spatial and Source Cue Based Source Separation for Dialog Enhancement, 2023, arXiv (Cornell University)

Frequent coauthors of Lie Lu include Aaron Master, Nathan Swedlow, Zeyu Luan, Yong Jiang, and Eloy Geenjaar. Collaboration with these researchers appears multiple times over the span of their publications.

Best Publications

  • A user attention model for video summarization

    Yu-Fei Ma;Lie Lu;Hong-Jiang Zhang;Mingjing Li

  • Content analysis for audio classification and segmentation

    Lie Lu;Hong-Jiang Zhang;Hao Jiang

  • Automatic mood detection and tracking of music audio signals

    Lie Lu;D. Liu;Hong-Jiang Zhang

  • A generic framework of user attention model and its application in video summarization

    Yu-Fei Ma;Xian-Sheng Hua;Lie Lu;Hong-Jiang Zhang

  • Music type classification by spectral contrast feature

    Dan-Ning Jiang;Lie Lu;Hong-Jiang Zhang;Jian-Hua Tao

  • A robust audio classification and segmentation method

    Lie Lu;Hao Jiang;HongJiang Zhang

  • Content-based audio classification and segmentation by using support vector machines

    Lie Lu;Hong-Jiang Zhang;Stan Z. Li

  • Automatic Mood Detection from Acoustic Music Data

    Dan Liu;Lie Lu;Hong-Jiang Zhang

  • Internet search-based television

    Frank T. B. Seide;Lie Lu;Neema M. Moraveji;Roger Peng Yu

  • A flexible framework for key audio effects detection and auditory context inference

    R. Cai;Lie Lu;A. Hanjalic;Hong-Jiang Zhang

  • Highlight sound effects detection in audio stream

    Rui Cai;Lie Lu;Hong-Jiang Zhang;Lian-Hong Cai

  • Automatic detection and segmentation of music videos in an audio/video stream

    Lie Lu;Yan-Feng Sun;Mingjing Li;Xian-Sheng Hua

  • A new approach to query by humming in music retrieval

    Lie Lu;Hong You;Hong-Jiang Zhang

  • Content-based audio segmentation using support vector machines

    Lie Lu;Stan Z. Li;Hong-Jiang Zhang

  • Speaker change detection and tracking in real-time news broadcasting analysis

    Lie Lu;Hong-Jiang Zhang

  • Automatic Music Mood Detection

    Lie Lu;Hong-Jiang Zhang

  • Repeating pattern discovery and structure analysis from acoustic music data

    Lie Lu;Muyuan Wang;Hong-Jiang Zhang

  • System and method for editing video automatically

    Xian-Sheng Hua;Mingjing Li;Lie Lu;Yu-Fei Ma

  • AVE: automated home video editing

    Xian-Sheng Hua;Lie Lu;Hong-Jiang Zhang

  • Systems and methods for generating a comprehensive user attention model

    Yu-Fei Ma;Lie Lu;Hong-Jiang Zhang

Frequent Co-Authors

Alan Hanjalic
Alan Hanjalic Delft University of Technology
Frank Seide
Frank Seide Microsoft (United States)
Xian-Sheng Hua
Xian-Sheng Hua Terminus International
Mingjing Li
Mingjing Li Microsoft (United States)
Stan Z. Li
Stan Z. Li Westlake University
Liu Wenyin
Liu Wenyin Guangdong University of Technology
Wei-Ying Ma
Wei-Ying Ma Tsinghua University
Xing Xie
Xing Xie Microsoft Research Asia (China)
Changshui Zhang
Changshui Zhang Tsinghua University

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