World's Best Scientists 2026 revealed!
Roland Badeau

Roland Badeau

D-Index & Metrics

Computer Science

D-Index
39
Citations
5194
World Ranking
9884
National Ranking
236

Overview

Roland Badeau is affiliated with Télécom ParisTech in France and has contributed extensively to research in signal processing and acoustic wave phenomena. Their work spans multiple fields including computer science and engineering, with specific interest in signal processing, biomedical engineering, artificial intelligence, oceanography, and control and systems engineering.

Their research primarily covers topics such as speech and audio processing, acoustic wave phenomena, music and audio processing, underwater acoustics, target tracking and data fusion in sensor networks, Gaussian processes and Bayesian inference, and blind source separation techniques.

Roland Badeau has published in several academic venues, with frequent contributions to:

  • The Journal of the Acoustical Society of America
  • arXiv (Cornell University)
  • HAL (Le Centre pour la Communication Scientifique Directe)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • International Symposium on the Acoustics of Ancient Theatres

Recent notable publications include:

  • "Statistical wave field theory," 2024, The Journal of the Acoustical Society of America
  • "Approximate Inference and Learning of State Space Models With Laplace Noise," 2021, IEEE Transactions on Signal Processing
  • "Unsupervised Music Source Separation Using Differentiable Parametric Source Models," 2023, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "Unsupervised Blind Source Separation with Variational Auto-Encoders," 2021, 2021 29th European Signal Processing Conference (EUSIPCO)
  • "Phoneme Level Lyrics Alignment and Text-Informed Singing Voice Separation," 2021, IEEE/ACM Transactions on Audio Speech and Language Processing

Roland Badeau collaborates frequently with several researchers, including:

  • Kilian Schulze-Forster
  • Gaël Richard
  • Clement S. J. Doire
  • Jean-Dominique Polack
  • Julian Neri

Their work involves methodologies and practices related to signal processing and audio analysis, often intersecting with machine learning and statistical modeling for audio source separation and acoustic wavefield analysis.

Best Publications

  • Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle

    Valentin Emiya;Roland Badeau;Bertrand David

  • Adaptive Harmonic Spectral Decomposition for Multiple Pitch Estimation

    E. Vincent;N. Bertin;R. Badeau

  • Enforcing Harmonicity and Smoothness in Bayesian Non-Negative Matrix Factorization Applied to Polyphonic Music Transcription

    N. Bertin;R. Badeau;E. Vincent

  • Fast approximated power iteration subspace tracking

    R. Badeau;B. David;G. Richard

  • Harmonic and inharmonic Nonnegative Matrix Factorization for Polyphonic Pitch transcription

    E. Vincent;N. Berlin;R. Badeau

  • Generalized Sliced Wasserstein Distances

    Soheil Kolouri;Kimia Nadjahi;Umut Simsekli;Roland Badeau

  • Gaussian Processes for Underdetermined Source Separation

    Antoine Liutkus;Roland Badeau;Gäel Richard

  • Generalized Wiener filtering with fractional power spectrograms

    Antoine Liutkus;Roland Badeau

  • Score informed audio source separation using a parametric model of non-negative spectrogram

    Romain Hennequin;Bertrand David;Roland Badeau

  • A new perturbation analysis for signal enumeration in rotational invariance techniques

    R. Badeau;B. David;G. Richard

  • Informed source separation through spectrogram coding and data embedding

    Antoine Liutkus;Jonathan Pinel;Roland Badeau;Laurent Girin

  • Singing voice detection with deep recurrent neural networks

    Simon Leglaive;Romain Hennequin;Roland Badeau

  • Adaptive filtering for music/voice separation exploiting the repeating musical structure

    Antoine Liutkus;Zafar Rafii;Roland Badeau;Bryan Pardo

  • Sliding window adaptive SVD algorithms

    R. Badeau;G. Richard;B. David

  • Blind Signal Decompositions for Automatic Transcription of Polyphonic Music: NMF and K-SVD on the Benchmark

    N. Bertin;R. Badeau;G. Richard

  • High-resolution spectral analysis of mixtures of complex exponentials modulated by polynomials

    R. Badeau;B. David;G. Richard

  • Fast Multilinear Singular Value Decomposition for Structured Tensors

    Roland Badeau;Rémy Boyer

  • NMF With Time–Frequency Activations to Model Nonstationary Audio Events

    Romain Hennequin;Roland Badeau;Bertrand David

  • Informed source separation: Source coding meets source separation

    Alexey Ozerov;Antoine Liutkus;Roland Badeau;Gael Richard

  • Stability Analysis of Multiplicative Update Algorithms and Application to Nonnegative Matrix Factorization

    R Badeau;N Bertin;E Vincent

  • Enforcing harmonicity and smoothness in Bayesian non-negative matrix factorization applied to polyphonic music transcription Contraintes d'harmonicité et de régularité temporelle dans les approches bayésiennes de la factorisation en matrices coefficients positifs appliquées à la transcription de musique polyphonique

    Nancy Bertin;Roland Badeau;Emmanuel Vincent

Frequent Co-Authors

Gael Richard
Gael Richard Télécom ParisTech
Bertrand David
Bertrand David Télécom ParisTech
Emmanuel Vincent
Emmanuel Vincent University of Lorraine
Laurent Girin
Laurent Girin Grenoble Institute of Technology
Laurent Daudet
Laurent Daudet Université Paris Cité
Mark D. Plumbley
Mark D. Plumbley King's College London
Gustavo K. Rohde
Gustavo K. Rohde University of Virginia
Eric Moulines
Eric Moulines Mohamed bin Zayed University of Artificial Intelligence
Karim Abed-Meraim
Karim Abed-Meraim University of Orléans

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