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D-Index & Metrics

Computer Science

D-Index
49
Citations
11828
World Ranking
5810
National Ranking
2641

Overview

Jonathan Le Roux is affiliated with Mitsubishi Electric (United States) and has contributed extensively to research in computer science, specifically focusing on signal processing and artificial intelligence. Their work spans a range of subfields including signal processing, artificial intelligence, computer vision and pattern recognition, computational mechanics, and cognitive neuroscience.

The main topics addressed in Jonathan Le Roux's research include:

  • Speech and Audio Processing
  • Music and Audio Processing
  • Speech Recognition and Synthesis
  • Advanced Adaptive Filtering Techniques
  • Multimodal Machine Learning Applications
  • Human Pose and Action Recognition
  • Hearing Loss and Rehabilitation

Le Roux's recent publications illustrate their focus on audio and speech-related technologies. Notable papers include:

  • "STFT-Domain Neural Speech Enhancement With Very Low Algorithmic Latency," 2022, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "(2.5+1)D Spatio-Temporal Scene Graphs for Video Question Answering," 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • "The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks," 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • "Convolutive Prediction for Monaural Speech Dereverberation and Noisy-Reverberant Speaker Separation," 2021, IEEE/ACM Transactions on Audio Speech and Language Processing

Frequently collaborating with other researchers, Le Roux's notable coauthors include:

  • Gordon Wichern
  • Zhong-Qiu Wang
  • Takaaki Hori
  • Chiori Hori
  • Darius Petermann

The scientist's work has appeared repeatedly in several prominent venues, reflecting a consistent engagement with leading conferences and journals in their domains. These venues include:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • Proceedings of the AAAI Conference on Artificial Intelligence

Best Publications

  • Deep clustering: Discriminative embeddings for segmentation and separation

    John R. Hershey;Zhuo Chen;Jonathan Le Roux;Shinji Watanabe

  • SDR – Half-baked or Well Done?

    Jonathan Le Roux;Scott Wisdom;Hakan Erdogan;John R. Hershey

  • Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks

    Hakan Erdogan;John R. Hershey;Shinji Watanabe;Jonathan Le Roux

  • Speech Enhancement with LSTM Recurrent Neural Networks and its Application to Noise-Robust ASR

    Felix Weninger;Hakan Erdogan;Shinji Watanabe;Emmanuel Vincent

  • Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures

    John R. Hershey;Jonathan Le Roux;Felix Weninger

  • Single-Channel Multi-Speaker Separation using Deep Clustering

    Yusuf Ziya Isik;Yusuf Ziya Isik;Jonathan Le Roux;Zhuo Chen;Zhuo Chen;Shinji Watanabe

  • Improved MVDR beamforming using single-channel mask prediction networks

    Hakan Erdogan;John R. Hershey;Shinji Watanabe;Michael I. Mandel

  • The second ‘chime’ speech separation and recognition challenge: Datasets, tasks and baselines

    Emmanuel Vincent;Jon Barker;Shinji Watanabe;Jonathan Le Roux

  • Discriminatively trained recurrent neural networks for single-channel speech separation

    Felix Weninger;John R. Hershey;Jonathan Le Roux;Bjorn Schuller

  • Phase Processing for Single-Channel Speech Enhancement: History and recent advances

    Timo Gerkmann;Martin Krawczyk-Becker;Jonathan Le Roux

  • WHAM!: Extending Speech Separation to Noisy Environments

    Gordon Wichern;Joe Antognini;Michael Flynn;Licheng Richard Zhu

  • Multi-Channel Deep Clustering: Discriminative Spectral and Spatial Embeddings for Speaker-Independent Speech Separation

    Zhong-Qiu Wang;Jonathan Le Roux;John R. Hershey

  • Full-capacity unitary recurrent neural networks

    Scott Wisdom;Thomas Powers;John R. Hershey;Jonathan Le Roux

  • Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error

    E. McDermott;T.J. Hazen;J. Le Roux;A. Nakamura

  • Alternative Objective Functions for Deep Clustering

    Zhong-Qiu Wang;Jonathan Le Roux;John R. Hershey

  • Separation of a monaural audio signal into harmonic/percussive components by complementary diffusion on spectrogram

    Nobutaka Ono;Kenichi Miyamoto;Jonathan Le Roux;Hirokazu Kameoka

  • Deep clustering and conventional networks for music separation: Stronger together

    Yi Luo;Zhuo Chen;John R. Hershey;Jonathan Le Roux

  • Universal Sound Separation

    Ilya Kavalerov;Scott Wisdom;Hakan Erdogan;Brian Patton

  • Deep NMF for speech separation

    Jonathan Le Roux;John R. Hershey;Felix Weninger

  • End-to-End Speech Separation with Unfolded Iterative Phase Reconstruction

    Zhong-Qiu Wang;Jonathan Le Roux;DeLiang Wang;John R. Hershey

  • Streaming automatic speech recognition with the transformer model

    Niko Moritz;Takaaki Hori;Jonathan Le Roux

Frequent Co-Authors

John R. Hershey
John R. Hershey Google (United States)
Shinji Watanabe
Shinji Watanabe Carnegie Mellon University
Hirokazu Kameoka
Hirokazu Kameoka NTT (Japan)
Shigeki Sagayama
Shigeki Sagayama University of Tokyo
Nobutaka Ono
Nobutaka Ono Tokyo Metropolitan University
Hakan Erdogan
Hakan Erdogan Google (United States)
Emmanuel Vincent
Emmanuel Vincent University of Lorraine
Felix Weninger
Felix Weninger Nuance Communications (United States)
Yanmin Qian
Yanmin Qian Shanghai Jiao Tong University
Alain de Cheveigné
Alain de Cheveigné École Normale Supérieure

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