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    AuthorTitleYearJournal/Proceedings/InstitutionBibTeX typeURL
    C. Castera, J. Bolte, C. Févotte & E. Pauwels An inertial Newton algorithm for deep learning to appear Journal of Machine Learning Research   article [PDF]  
    BibTeX:
    
    @article{Castera2021a,
      author = {C. Castera and J. Bolte and C. Févotte and E. Pauwels} ,
      title = {An inertial Newton algorithm for deep learning},
      journal = {Journal of Machine Learning Research},
      year = {to appear},
      url = {https://arxiv.org/pdf/1905.12278}
    }
    
    C. Dantas, E. Soubies & C. Févotte Expanding boundaries of Gap Safe screening to appear Journal of Machine Learning Research   article [PDF]  
    BibTeX:
    
    @article{Dantasto appear,
      author = {Dantas, C. and Soubies, E. and Févotte, C.} ,
      title = {Expanding boundaries of Gap Safe screening},
      journal = {Journal of Machine Learning Research},
      year = {to appear},
      url = {https://arxiv.org/abs/2102.10846}
    }
    
    T. Cai, V.Y.F. Tan & C. Févotte Adversarially-trained nonnegative matrix factorization 2021 IEEE Signal Processing Letters   article [PDF]  
    BibTeX:
    
    @article{Cai2021,
      author = {Cai, T. and Tan, V. Y. F. and Févotte, C.} ,
      title = {Adversarially-trained nonnegative matrix factorization},
      journal = {IEEE Signal Processing Letters},
      year = {2021},
      url = {https://arxiv.org/pdf/2104.04757}
    }
    
    L. Filstroff, O. Gouvert, C. Févotte & O. Cappé A comparative study of Gamma Markov chains for temporal non-negative factorization 2021 IEEE Transactions on Signal Processing   article [PDF] [DOI]  
    BibTeX:
    
    @article{Filstroff2021,
      author = {L. Filstroff and O. Gouvert and C. Févotte and O. Cappé} ,
      title = {A comparative study of Gamma Markov chains for temporal non-negative factorization},
      journal = {IEEE Transactions on Signal Processing},
      year = {2021},
      url = {https://arxiv.org/pdf/2006.12843},
      doi = {https://doi.org/10.1109/TSP.2021.3060000}
    }
    
    D. Lahat, Y. Lang, V.Y.F. Tan & C. Févotte Positive semidefinite matrix factorization: A connection with phase retrieval and affine rank minimization 2021 IEEE Transactions on Signal Processing   article [PDF] [DOI]  
    BibTeX:
    
    @article{Lahat2021,
      author = {D. Lahat and Y. Lang and V. Y. F. Tan and C. Févotte} ,
      title = {Positive semidefinite matrix factorization: A connection with phase retrieval and affine rank minimization},
      journal = {IEEE Transactions on Signal Processing},
      year = {2021},
      volume = {69},
      pages = {3059--3074},
      url = {https://arxiv.org/pdf/2007.12364},
      doi = {https://doi.org/10.1109/TSP.2021.3071293}
    }
    
    P.-H. Vial, P. Magron, T. Oberlin & C. Févotte Phase retrieval with Bregman divergences and application to audio signal recovery 2021 IEEE Journal of Selected Topics in Signal Processing   article [PDF] [DOI]  
    BibTeX:
    
    @article{Vial2021,
      author = {P.-H. Vial and P. Magron and T. Oberlin and C. Févotte} ,
      title = {Phase retrieval with Bregman divergences and application to audio signal recovery},
      journal = {IEEE Journal of Selected Topics in Signal Processing},
      month = {January},
      year = {2021},
      volume = {15},
      number = {1},
      pages = {51--64},
      url = {https://arxiv.org/pdf/2010.00392},
      doi = {https://doi.org/10.1109/JSTSP.2021.3051870}
    }
    
    C. Dantas, E. Soubies & C. Févotte Safe screening for sparse regression with the Kullback-Leibler divergence 2021 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Dantas2021,
      author = {C. Dantas and E. Soubies and C. Févotte} ,
      title = {Safe screening for sparse regression with the Kullback-Leibler divergence},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      month = {June},
      year = {2021},
      url = {https://hal.archives-ouvertes.fr/hal-03147345/document}
    }
    
    P. Magron & C. Févotte Leveraging the structure of musical preference in content-aware music recommendation 2021 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Magron2021,
      author = {P. Magron and C. Févotte} ,
      title = {Leveraging the structure of musical preference in content-aware music recommendation},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      month = {June},
      year = {2021},
      url = {https://arxiv.org/pdf/2010.10276}
    }
    
    P. Magron, P.-H. Vial, T. Oberlin & C. Févotte Phase recovery with Bregman divergences for audio source separation 2021 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Magron2021a,
      author = {Magron, P. and Vial, P.-H. and Oberlin, T. and Févotte, C.} ,
      title = {Phase recovery with Bregman divergences for audio source separation},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      month = {June},
      year = {2021},
      url = {https://arxiv.org/pdf/2010.10255}
    }
    
    L. Chapel, R. Flamary, H. Wu, C. Févotte & G. Gasso Unbalanced optimal transport through non-negative penalized linear regression 2021 arXiv   techreport [PDF]  
    BibTeX:
    
    @techreport{Chapel2021,
      author = {L. Chapel and R. Flamary and H. Wu and C. Févotte and G. Gasso} ,
      title = {Unbalanced optimal transport through non-negative penalized linear regression},
      institution = {arXiv},
      month = {June},
      year = {2021},
      url = {https://arxiv.org/pdf/2106.04145}
    }
    
    A. Marmin, J.H. de.M. Goulart & C. Févotte Joint majorization-minimization for nonnegative matrix factorization with the beta-divergence 2021 arXiv   techreport [PDF]  
    BibTeX:
    
    @techreport{Marmin2021,
      author = {A. Marmin and J. H. de. M. Goulart and C. Févotte} ,
      title = {Joint majorization-minimization for nonnegative matrix factorization with the beta-divergence},
      institution = {arXiv},
      month = {June},
      year = {2021},
      url = {https://arxiv.org/pdf/2106.15214}
    }
    
    C. Castera, J. Bolte, C. Févotte & E. Pauwels Second-order step-size tuning of SGD for non-convex optimization 2021 arXiv   techreport [PDF]  
    BibTeX:
    
    @techreport{Castera2021,
      author = {Castera, C. and Bolte, J. and Févotte, C. and Pauwels, E.} ,
      title = {Second-order step-size tuning of SGD for non-convex optimization},
      institution = {arXiv},
      month = {Mar.},
      year = {2021},
      url = {https://arxiv.org/abs/2103.03570}
    }
    
    P. Magron & C. Févotte Neural content-aware collaborative filtering for cold-start music recommendation 2021 arXiv   techreport [PDF]  
    BibTeX:
    
    @techreport{Magron2021b,
      author = {Magron, P. and Févotte, C.} ,
      title = {Neural content-aware collaborative filtering for cold-start music recommendation},
      institution = {arXiv},
      month = {Feb.},
      year = {2021},
      url = {https://arxiv.org/abs/2102.12369}
    }
    
    O. Gouvert, T. Oberlin & C. Févotte Negative binomial matrix factorization 2020 IEEE Signal Processing Letters   article [PDF] [DOI]  
    BibTeX:
    
    @article{Gouvert2020,
      author = {O. Gouvert and T. Oberlin and C. Févotte} ,
      title = {Negative binomial matrix factorization},
      journal = {IEEE Signal Processing Letters},
      month = {Dec.},
      year = {2020},
      volume = {27},
      pages = {815-819},
      url = {https://hal.archives-ouvertes.fr/hal-02871905/document},
      doi = {https://doi.org/10.1109/LSP.2020.2991613}
    }
    
    A. Lumbreras, L. Filstroff & C. Févotte Bayesian mean-parameterized nonnegative binary matrix factorization 2020 Data Mining and Knowledge Discovery   article [PDF] [DOI]  
    BibTeX:
    
    @article{Lumbreras2020,
      author = {A. Lumbreras and L. Filstroff and C. Févotte} ,
      title = {Bayesian mean-parameterized nonnegative binary matrix factorization},
      journal = {Data Mining and Knowledge Discovery},
      month = {Nov.},
      year = {2020},
      volume = {34},
      number = {6},
      pages = {1898--1935},
      url = {https://arxiv.org/pdf/1812.06866},
      doi = {https://doi.org/10.1007/s10618-020-00712-w}
    }
    
    S. Zhang, E. Soubies & C. Févotte On the identifiability of transform learning for non-negative matrix factorization 2020 IEEE Signal Processing Letters   article [PDF] [DOI]  
    BibTeX:
    
    @article{Zhang2020,
      author = {S.  Zhang and E. Soubies and C. Févotte} ,
      title = {On the identifiability of transform learning for non-negative matrix factorization},
      journal = {IEEE Signal Processing Letters},
      month = {Aug.},
      year = {2020},
      volume = {27},
      pages = {1555--1559},
      url = {https://hal.archives-ouvertes.fr/hal-02542653/document},
      doi = {https://doi.org/10.1109/LSP.2020.3020431}
    }
    
    M. Devy, F. Lerasle, N. Dobigeon & C. Févotte Perception et apprentissage de représentations 2020 Intelligence artificielle - Regards croisés de chercheur.es   incollection  
    BibTeX:
    
    @incollection{Devy2021,
      author = {Devy, M. and Lerasle, F. and Dobigeon, N. and Févotte, C.} ,
      title = {Perception et apprentissage de représentations},
      booktitle = {Intelligence artificielle - Regards croisés de chercheur.es},
      publisher = {La Dépêche; CNRS},
      year = {2020}
    }
    
    D. Lahat & C. Févotte Positive semidefinite matrix factorization based on truncated Wirtinger flow 2020 Proc. European Signal Processing Conference (EUSIPCO)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Lahat2020a,
      author = {D. Lahat and C. Févotte} ,
      title = {Positive semidefinite matrix factorization based on truncated Wirtinger flow},
      booktitle = {Proc. European Signal Processing Conference (EUSIPCO)},
      year = {2020},
      url = {https://hal.archives-ouvertes.fr/hal-02886419/document}
    }
    
    O. Gouvert, T. Oberlin & C. Févotte Ordinal non-negative matrix factorization for recommendation 2020 Proc. International Conference on Machine Learning (ICML)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Gouvert2020a,
      author = {O. Gouvert and T. Oberlin and C. Févotte} ,
      title = {Ordinal non-negative matrix factorization for recommendation},
      booktitle = {Proc. International Conference on Machine Learning (ICML)},
      month = {July},
      year = {2020},
      url = {https://arxiv.org/pdf/2006.01034}
    }
    
    D. Lahat & C. Févotte Positive semidefinite matrix factorization: link to phase retrieval and a block gradient algorithm 2020 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF] [DOI]  
    BibTeX:
    
    @inproceedings{Lahat2020,
      author = {D. Lahat and C. Févotte} ,
      title = {Positive semidefinite matrix factorization: link to phase retrieval and a block gradient algorithm},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      month = {May},
      year = {2020},
      url = {https://hal.archives-ouvertes.fr/hal-02875241/document},
      doi = {https://doi.org/10.1109/ICASSP40776.2020.9053938}
    }
    
    V. Leplat, N. Gillis & C. Févotte Multi-resolution beta-divergence NMF for blind spectral unmixing 2020 arXiv   techreport [PDF]  
    BibTeX:
    
    @techreport{Leplat2020,
      author = {V. Leplat and N. Gillis and C. Févotte} ,
      title = {Multi-resolution beta-divergence NMF for blind spectral unmixing},
      institution = {arXiv},
      month = {July},
      year = {2020},
      url = {https://arxiv.org/pdf/2007.03893}
    }
    
    Y.C. Cavalcanti, T. Oberlin, N. Dobigeon, C. Févotte, S. Stute, M. Ribeiro & C. Tauber Factor analysis of dynamic PET images: Beyond Gaussian noise 2019 IEEE Transactions on Medical Imaging   article [PDF] [DOI]  
    BibTeX:
    
    @article{Cavalcanti2019a,
      author = {Y. C. Cavalcanti and T. Oberlin and N. Dobigeon and C. Févotte and S. Stute and M. Ribeiro and C. Tauber} ,
      title = {Factor analysis of dynamic PET images: Beyond Gaussian noise},
      journal = {IEEE Transactions on Medical Imaging},
      month = {Sep.},
      year = {2019},
      volume = {38},
      number = {9},
      pages = {2231--2241},
      url = {https://arxiv.org/pdf/1807.11455},
      doi = {https://doi.org/10.1109/TMI.2019.2906828}
    }
    
    C. Castera, J. Bolte, C. Févotte & E. Pauwels An inertial Newton algorithm for deep learning 2019 Proc. NeurIPS Workshop ``Beyond First Order Methods in ML"   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Castera2019,
      author = {C. Castera and J. Bolte and C. Févotte and E. Pauwels} ,
      title = {An inertial Newton algorithm for deep learning},
      booktitle = {Proc. NeurIPS Workshop ``Beyond First Order Methods in ML"},
      month = {Dec.},
      year = {2019},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/beyondFO2019.pdf}
    }
    
    R. Xia, V.Y.F. Tan, L. Filstroff & C. Févotte A ranking model motivated by nonnegative matrix factorization with applications to tennis tournaments 2019 Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Xia2019,
      author = {R. Xia and V. Y. F. Tan and L. Filstroff and C. Févotte} ,
      title = {A ranking model motivated by nonnegative matrix factorization with applications to tennis tournaments},
      booktitle = {Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)},
      month = {Sep.},
      year = {2019},
      url = {https://arxiv.org/abs/1903.06500}
    }
    
    O. Gouvert, T. Oberlin & C. Févotte Recommendation from raw data with adaptive compound Poisson factorization 2019 Proc. Conference on Uncertainty in Artificial Intelligence (UAI)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Gouvert2019,
      author = {O. Gouvert and T. Oberlin and C. Févotte} ,
      title = {Recommendation from raw data with adaptive compound Poisson factorization},
      booktitle = {Proc. Conference on Uncertainty in Artificial Intelligence (UAI)},
      month = {July},
      year = {2019},
      url = {https://arxiv.org/pdf/1905.13128}
    }
    
    P. Ablin, D. Fagot, H. Wendt, A. Gramfort & C. Févotte A quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning 2019 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF] [CODE]  
    BibTeX:
    
    @inproceedings{Ablin2019,
      author = {P. Ablin and D. Fagot and H. Wendt and A. Gramfort and C. Févotte} ,
      title = {A quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      month = {May},
      year = {2019},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp2019b.pdf}
    }
    
    Y.C. Cavalcanti, T. Oberlin, N. Dobigeon, C. Févotte, S. Stute & C. Tauber Unmixing dynamic PET images: Combining spatial heterogeneity and non-gaussian noise 2019 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Cavalcanti2019,
      author = {Y. C. Cavalcanti and T. Oberlin and N. Dobigeon and C. Févotte and S. Stute and C. Tauber} ,
      title = {Unmixing dynamic PET images: Combining spatial heterogeneity and non-gaussian noise},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      month = {May},
      year = {2019},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp2019c.pdf}
    }
    
    D. Fagot, H. Wendt, C. Févotte & P. Smaragdis Majorization-minimization algorithms for convolutive NMF with the beta-divergence 2019 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF] [CODE]  
    BibTeX:
    
    @inproceedings{Fagot2019,
      author = {D. Fagot and H. Wendt and C. Févotte and P. Smaragdis} ,
      title = {Majorization-minimization algorithms for convolutive NMF with the beta-divergence},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      month = {May},
      year = {2019},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp2019a.pdf}
    }
    
    C. Févotte & M. Kowalski Estimation with low-rank time-frequency synthesis models 2018 IEEE Transactions on Signal Processing   article [PDF] [DOI] [DEMO]  
    BibTeX:
    
    @article{Fevotte2018b,
      author = {C. Févotte and M. Kowalski} ,
      title = {Estimation with low-rank time-frequency synthesis models},
      journal = {IEEE Transactions on Signal Processing},
      month = {Aug.},
      year = {2018},
      volume = {66},
      number = {15},
      pages = {4121--4132},
      url = {https://arxiv.org/pdf/1804.09497},
      doi = {https://doi.org/10.1109/TSP.2018.2844159}
    }
    
    C. Févotte, E. Vincent & A. Ozerov Single-channel audio source separation with NMF: divergences, constraints and algorithms 2018 Audio Source Separation   incollection [PDF]  
    BibTeX:
    
    @incollection{Fevotte2018a,
      author = {C. Févotte and E. Vincent and A. Ozerov} ,
      title = {Single-channel audio source separation with NMF: divergences, constraints and algorithms},
      booktitle = {Audio Source Separation},
      editor = {S. Makino},
      publisher = {Springer},
      year = {2018},
      pages = {1--24},
      url = {https://hal.inria.fr/hal-01631185/file/fevotte_book18.pdf}
    }
    
    A. Ozerov, C. Févotte & E. Vincent An introduction to multichannel NMF for audio source separation 2018 Audio Source Separation   incollection [PDF]  
    BibTeX:
    
    @incollection{Ozerov2018,
      author = {A. Ozerov and C. Févotte and E. Vincent} ,
      title = {An introduction to multichannel NMF for audio source separation},
      booktitle = {Audio Source Separation},
      editor = {S. Makino},
      publisher = {Springer},
      year = {2018},
      pages = {73--94},
      url = {https://hal.inria.fr/hal-01631187/file/ozerov_book18.pdf}
    }
    
    C. Févotte, P. Smaragdis, N. Mohammadiha & G. Mysore Temporal extensions of nonnegative matrix factorization 2018 Audio Source Separation and Speech Enhancement   incollection [PDF]  
    BibTeX:
    
    @incollection{Fevotte2018,
      author = {C. Févotte and P. Smaragdis and N. Mohammadiha and G. Mysore} ,
      title = {Temporal extensions of nonnegative matrix factorization},
      booktitle = {Audio Source Separation and Speech Enhancement},
      editor = {E. Vincent and T. Virtanen and S. Gannot},
      publisher = {Wiley},
      month = {Sep.},
      year = {2018},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/chapters/asssp2018.pdf}
    }
    
    O. Gouvert, T. Oberlin & C. Févotte Matrix co-factorization for cold-start recommendation 2018 Proc. International Society for Music Information Retrieval Conference (ISMIR)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Gouvert2018a,
      author = {O. Gouvert and T. Oberlin and C. Févotte} ,
      title = {Matrix co-factorization for cold-start recommendation},
      booktitle = {Proc. International Society for Music Information Retrieval Conference (ISMIR)},
      month = {Sep.},
      year = {2018},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/ismir2018.pdf}
    }
    
    H. Wendt, D. Fagot & C. Févotte Jacobi algorithm for nonnegative matrix factorization with transform learning 2018 Proc. European Signal Processing Conference (EUSIPCO)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Wendt2018,
      author = {H. Wendt and D. Fagot and C. Févotte} ,
      title = {Jacobi algorithm for nonnegative matrix factorization with transform learning},
      booktitle = {Proc. European Signal Processing Conference (EUSIPCO)},
      month = {Sep.},
      year = {2018},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/eusipco2018.pdf}
    }
    
    L. Filstroff, A. Lumbreras & C. Févotte Closed-form marginal likelihood in Gamma-Poisson matrix factorizations 2018 Proc. International Conference on Machine Learning (ICML)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Filstroff2018,
      author = {L. Filstroff and A. Lumbreras and C. Févotte} ,
      title = {Closed-form marginal likelihood in Gamma-Poisson matrix factorizations},
      booktitle = {Proc. International Conference on Machine Learning (ICML)},
      month = {July},
      year = {2018},
      url = {https://arxiv.org/pdf/1801.01799}
    }
    
    D. Fagot, H. Wendt & C. Févotte Nonnegative matrix factorization with transform learning 2018 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Fagot2018,
      author = {D. Fagot and H. Wendt and C. Févotte} ,
      title = {Nonnegative matrix factorization with transform learning},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      month = {Apr.},
      year = {2018},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp18.pdf}
    }
    
    O. Gouvert, T. Oberlin & C. Févotte Negative binomial matrix factorization for recommender systems 2018 arXiv   techreport [PDF]  
    BibTeX:
    
    @techreport{Gouvert2018,
      author = {O. Gouvert and T. Oberlin and C. Févotte} ,
      title = {Negative binomial matrix factorization for recommender systems},
      institution = {arXiv},
      year = {2018},
      url = {https://arxiv.org/pdf/1801.01708}
    }
    
    R. Flamary, C. Févotte, N. Courty & V. Emiya Optimal spectral transportation with application to music transcription 2016 Advances in Neural Information Processing Systems (NIPS)   inproceedings [PDF] [CODE]  
    BibTeX:
    
    @inproceedings{Flamary2016,
      author = {R. Flamary and C. Févotte and N. Courty and V. Emiya} ,
      title = {Optimal spectral transportation with application to music transcription},
      booktitle = {Advances in Neural Information Processing Systems (NIPS)},
      month = {Dec.},
      year = {2016},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/nips16.pdf}
    }
    
    R. Hamon, V. Emiya & C. Févotte Convex nonnegative matrix factorization with missing data 2016 Proc. IEEE International Workshop on Machine Learning for Signal Processing (MLSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Hanon2016,
      author = {R. Hamon and V. Emiya and C. Févotte} ,
      title = {Convex nonnegative matrix factorization with missing data},
      booktitle = {Proc. IEEE International Workshop on Machine Learning for Signal Processing (MLSP)},
      month = {Sep.},
      year = {2016},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/mlsp16.pdf}
    }
    
    R. Hamon, V. Emiya & C. Févotte Factorisation archétypale en matrices non-négatives avec données manquantes 2016 Proc. Conférence francophone sur l'Apprentissage Automatique (CAP)   inproceedings  
    BibTeX:
    
    @inproceedings{Hanon2016a,
      author = {R. Hamon and V. Emiya and C. Févotte} ,
      title = {Factorisation archétypale en matrices non-négatives avec données manquantes},
      booktitle = {Proc. Conférence francophone sur l'Apprentissage Automatique (CAP)},
      month = {Jul.},
      year = {2016}
    }
    
    C. Févotte & N. Dobigeon Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization 2015 IEEE Transactions on Image Processing   article [PDF] [DOI] [CODE]  
    BibTeX:
    
    @article{tip15,
      author = {Févotte, C. and Dobigeon, N.} ,
      title = {Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization},
      journal = {IEEE Transactions on Image Processing},
      month = {Dec.},
      year = {2015},
      volume = {24},
      number = {12},
      pages = {4810-4819},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/tip2015.pdf},
      doi = {https://doi.org/10.1109/TIP.2015.2468177}
    }
    
    C. Févotte & J. Grivel Apprentissage pour la synthèse visuelle guidée par un flux audio et application dans le champ de l'art contemporain 2015 Proc. Colloque GRETSI sur le Traitement du Signal et des Images   inproceedings [PDF] [DEMO]  
    BibTeX:
    
    @inproceedings{Fevotte2015,
      author = {C. Févotte and J. Grivel} ,
      title = {Apprentissage pour la synthèse visuelle guidée par un flux audio et application dans le champ de l'art contemporain},
      booktitle = {Proc. Colloque GRETSI sur le Traitement du Signal et des Images},
      address = {Lyon, France},
      month = {Sep.},
      year = {2015},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/gretsi15a.pdf}
    }
    
    C. Févotte & M. Kowalski Hybrid sparse and low-rank time-frequency signal decomposition 2015 Proc. European Signal Processing Conference (EUSIPCO)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Fevotte2015a,
      author = {C. Févotte and M. Kowalski} ,
      title = {Hybrid sparse and low-rank time-frequency signal decomposition},
      booktitle = {Proc. European Signal Processing Conference (EUSIPCO)},
      address = {Nice, France},
      month = {Sep.},
      year = {2015},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/eusipco15.pdf}
    }
    
    R. Hamon, P. Borgnat, C. Févotte, P. Flandrin & C. Robardet Factorisation de réseaux temporels : étude des rythmes hebdomadaires du système Vélo'v 2015 Proc. Colloque GRETSI sur le Traitement du Signal et des Images   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{Hanon2015,
      author = {R. Hamon and P. Borgnat and C. Févotte and P. Flandrin and C. Robardet} ,
      title = {Factorisation de réseaux temporels : étude des rythmes hebdomadaires du système Vélo'v},
      booktitle = {Proc. Colloque GRETSI sur le Traitement du Signal et des Images},
      address = {Lyon, France},
      month = {Sep.},
      year = {2015},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/gretsi15b.pdf}
    }
    
    P. Zarka & al. NENUFAR: Instrument description and science case 2015 Proc. International Conference on Antenna Theory and Techniques (ICATT)   inproceedings  
    BibTeX:
    
    @inproceedings{Zarka2015,
      author = {Zarka, P. and al.} ,
      title = {NENUFAR: Instrument description and science case},
      booktitle = {Proc. International Conference on Antenna Theory and Techniques (ICATT)},
      address = {Kharkiv, Ukraine},
      month = {Apr.},
      year = {2015}
    }
    
    N. Seichepine, S. Essid, C. Févotte & O. Cappé Soft nonnegative matrix co-factorization 2014 IEEE Transactions on Signal Processing   article [PDF]  
    BibTeX:
    
    @article{Seichepine2014,
      author = {N. Seichepine and S. Essid and C. Févotte and O. Cappé} ,
      title = {Soft nonnegative matrix co-factorization},
      journal = {IEEE Transactions on Signal Processing},
      month = {Nov.},
      year = {2014},
      volume = {62},
      number = {22},
      pages = {5940--5949},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/tsp2014.pdf}
    }
    
    P. Smaragdis, C. Févotte, G. Mysore, N. Mohammadiha & M. Hoffman Static and dynamic source separation using nonnegative factorizations: A unified view 2014 IEEE Signal Processing Magazine   article [PDF] [DOI]  
    BibTeX:
    
    @article{Smaragdis2014,
      author = {P. Smaragdis and C. Févotte and G. Mysore and N. Mohammadiha and M. Hoffman} ,
      title = {Static and dynamic source separation using nonnegative factorizations: A unified view},
      journal = {IEEE Signal Processing Magazine},
      month = {May},
      year = {2014},
      volume = {31},
      number = {3},
      pages = {66-75},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/spm2014.pdf},
      doi = {https://doi.org/10.1109/MSP.2013.2297715}
    }
    
    C. Févotte & M. Kowalski Low-rank time-frequency synthesis 2014 Advances in Neural Information Processing Systems (NIPS)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{nips14,
      author = {C. Févotte and M. Kowalski} ,
      title = {Low-rank time-frequency synthesis},
      booktitle = {Advances in Neural Information Processing Systems (NIPS)},
      month = {Dec.},
      year = {2014},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/nips14.pdf}
    }
    
    D.L. Sun & C. Févotte Alternating direction method of multipliers for non-negative matrix factorization with the beta-divergence 2014 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF] [CODE]  
    BibTeX:
    
    @inproceedings{icassp14a,
      author = {D. L. Sun and C. Févotte} ,
      title = {Alternating direction method of multipliers for non-negative matrix factorization with the beta-divergence},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Florence, Italy},
      month = {May},
      year = {2014},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp14a.pdf}
    }
    
    N. Seichepine, S. Essid, C. Févotte & O. Cappé Piecewise constant nonnegative matrix factorization 2014 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{icassp14b,
      author = {N. Seichepine and S. Essid and C. Févotte and O. Cappé} ,
      title = {Piecewise constant nonnegative matrix factorization},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Florence, Italy},
      month = {May},
      year = {2014},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp14b.pdf}
    }
    
    C. Févotte Contributions à la factorisation en matrices non-négatives 2014 Mémoire d'Habilitation à Diriger des Recherches. Université Nice Sophia Antipolis   misc [PDF] [SLIDES]  
    BibTeX:
    
    @misc{Fevotte2014a,
      author = {C. Févotte} ,
      title = {Contributions à la factorisation en matrices non-négatives},
      month = {Oct.},
      year = {2014},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/theses/hdr_fevotte.pdf},
      slides = {https://www.irit.fr/ Cedric.Fevotte/publications/theses/hdr_fevotte.pdf}
    }
    
    V.Y.F. Tan & C. Févotte Automatic relevance determination in nonnegative matrix factorization with the beta-divergence 2013 IEEE Transactions on Pattern Analysis and Machine Intelligence   article [PDF] [CODE]  
    Abstract: This paper addresses the problem of estimating the latent dimensionality
    in nonnegative matrix factorization (NMF). The estimation is done
    via automatic relevance determination (ARD). Uncovering the model
    order is important as it is necessary to strike the right balance
    between data fidelity and overfitting. We propose a Bayesian model
    for NMF and two families of algorithms known as l1-ARD and l2-ARD,
    each assuming different priors on the basis elements and the activation
    coefficients. The proposed algorithms leverage on the recent algorithmic
    advances in NMF with the beta-divergence using majorization-minimization
    (MM) methods. They are parametrized by the shape parameter beta which
    governs the assumption on the noise statistics. We show by deriving
    various auxiliary functions that the cost functions of the algorithms
    decrease monotonically to a local minimum. We also describe a heuristic
    to select the hyperparameters based on the method of moments. We
    demonstrate the efficacy and robustness of our algorithms by performing
    extensive experiments on synthetic data, the swimmer dataset, a music
    decomposition example and a stock price prediction task.
    BibTeX:
    
    @article{ardnmfj,
      author = {V. Y. F. Tan and C. Févotte} ,
      title = {Automatic relevance determination in nonnegative matrix factorization with the beta-divergence},
      journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      month = {July},
      year = {2013},
      volume = {35},
      number = {7},
      pages = {1592 -- 1605},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/pami13_ardnmf.pdf}
    }
    
    S. Essid & C. Févotte Smooth nonnegative matrix factorization for unsupervised audiovisual document structuring 2013 IEEE Transactions on Multimedia   article [PDF] [DOI]  
    Abstract: This paper introduces a new paradigm for unsupervised audiovisual
    document structuring. In this paradigm, a novel Nonnegative Matrix
    Factorization (NMF) algorithm is applied on histograms of counts
    (relating to a bag of features representation of the content) to
    jointly discover latent structuring patterns and their activations
    in time. Our NMF variant employs the Kullback-Leibler divergence
    as a cost function and imposes a temporal smoothness constraint to
    the activations. It is solved for using a majorization-minimization
    technique. The approach proposed is meant to be generic and is particularly
    well suited to applications where the structuring patterns may overlap
    in time. As such, it is evaluated on a person-oriented video structuring
    task, using a challenging database of political debate videos. Our
    results outperform reference results obtained by a method using Hidden
    Markov Models. Further, we show the potential that our general approach
    has for audio speaker diarization.
    BibTeX:
    
    @article{nmfvideostruct,
      author = {S. Essid and C. Févotte} ,
      title = {Smooth nonnegative matrix factorization for unsupervised audiovisual document structuring},
      journal = {IEEE Transactions on Multimedia},
      month = {Feb.},
      year = {2013},
      volume = {15},
      number = {2},
      pages = {415--425},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/ieee_multimedia_smoothnmf.pdf},
      doi = {https://doi.org/10.1109/TMM.2012.2228474}
    }
    
    N. Seichepine, S. Essid, C. Févotte & O. Cappé Co-factorisation douce en matrices non-négatives. Application au regroupement multimodal de locuteurs 2013 Proc. Colloque GRETSI sur le Traitement du Signal et des Images   inproceedings  
    BibTeX:
    
    @inproceedings{gretsi13,
      author = {N. Seichepine and S. Essid and C. Févotte and O. Cappé} ,
      title = {Co-factorisation douce en matrices non-négatives. Application au regroupement multimodal de locuteurs},
      booktitle = {Proc. Colloque GRETSI sur le Traitement du Signal et des Images},
      address = {Brest, France},
      month = {Sep.},
      year = {2013}
    }
    
    N. Dobigeon & C. Févotte Robust nonnegative matrix factorization for nonlinear unmixing of hyperspectral images 2013 Proc. IEEE Workshop Hyperspectral image and signal processing: Evolution in remote sensing (WHISPERS)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{whispers13,
      author = {Dobigeon, N. and Févotte, C.} ,
      title = {Robust nonnegative matrix factorization for nonlinear unmixing of hyperspectral images},
      booktitle = {Proc. IEEE Workshop Hyperspectral image and signal processing: Evolution in remote sensing (WHISPERS)},
      address = {Gainesville, Florida},
      month = {June},
      year = {2013},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/whispers13.pdf}
    }
    
    C. Févotte, J. Le Roux & J.R. Hershey Non-negative dynamical system with application to speech and audio 2013 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF] [CODE] [DEMO]  
    BibTeX:
    
    @inproceedings{icassp13a,
      author = {C. Févotte and J. Le Roux and J. R. Hershey} ,
      title = {Non-negative dynamical system with application to speech and audio},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Vancouver, Canada},
      month = {May},
      year = {2013},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp13a.pdf}
    }
    
    N. Seichepine, S. Essid, C. Févotte & O. Cappé Soft nonnegative matrix co-factorization with application to multimodal speaker diarization 2013 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{icassp13b,
      author = {N. Seichepine and S. Essid and C. Févotte and O. Cappé} ,
      title = {Soft nonnegative matrix co-factorization with application to multimodal speaker diarization},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Vancouver, Canada},
      month = {May},
      year = {2013},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp13b.pdf}
    }
    
    J. Le Roux, C. Févotte & J.R. Hershey A new non-negative dynamical system for speech and audio modeling 2013 Proc. Acoustical Society of Japan Spring Meeting   inproceedings  
    BibTeX:
    
    @inproceedings{LeRoux2013,
      author = {J. Le Roux and C. Févotte and J. R. Hershey} ,
      title = {A new non-negative dynamical system for speech and audio modeling},
      booktitle = {Proc. Acoustical Society of Japan Spring Meeting},
      month = {Mar.},
      year = {2013}
    }
    
    O. Dikmen & C. Févotte Maximum marginal likelihood estimation for nonnegative dictionary learning in the Gamma-Poisson Model 2012 IEEE Transactions on Signal Processing   article [PDF] [DOI] [CODE]  
    Abstract: In this paper we describe an alternative to standard nonnegative matrix
    factorization (NMF) for nonnegative dictionary learning, i.e., the
    task of learning a dictionary with nonnegative values from nonnegative
    data, under the assumption of nonnegative expansion coefficients.
    A popular cost function used for NMF is the Kullback-Leibler divergence,
    which underlies a Poisson observation model. NMF can thus be considered
    as maximization of the joint likelihood of the dictionary and the
    expansion coefficients. This approach lacks optimality because the
    number of parameters (which include the expansion coefficients) grows
    with the number of observations. In this paper we describe variational
    Bayes and Monte-Carlo EM algorithms for optimization of the marginal
    likelihood, i.e., the likelihood of the dictionary where the expansion
    coefficients have been integrated out (given a Gamma prior). We compare
    the output of both maximum joint likelihood estimation (i.e., standard
    NMF) and maximum marginal likelihood estimation (MMLE) on real and
    synthetical datasets. In particular we present face reconstruction
    results on CBCL dataset and text retrieval results over the musiXmatch
    dataset, a collection of word counts in song lyrics. The MMLE approach
    is shown to prevent overfitting by automatically pruning out irrelevant
    dictionary columns, i.e., embedding automatic model order selection.
    BibTeX:
    
    @article{tspgap,
      author = {O. Dikmen and C. Févotte} ,
      title = {Maximum marginal likelihood estimation for nonnegative dictionary learning in the Gamma-Poisson Model},
      journal = {IEEE Transactions on Signal Processing},
      month = {Oct.},
      year = {2012},
      volume = {60},
      number = {10},
      pages = {5163--5175},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/ieee_sp_mmle.pdf},
      doi = {https://doi.org/10.1109/TSP.2012.2207117}
    }
    
    A. Lefèvre, F. Bach & C. Févotte Semi-supervised NMF with time-frequency annotations for single-channel source separation 2012 Proc. International Society for Music Information Retrieval Conference (ISMIR)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{ismir12a,
      author = {A. Lefèvre and F. Bach and C. Févotte} ,
      title = {Semi-supervised NMF with time-frequency annotations for single-channel source separation},
      booktitle = {Proc. International Society for Music Information Retrieval Conference (ISMIR)},
      address = {Porto, Portugal},
      month = {Oct.},
      year = {2012},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/ismir12a.pdf}
    }
    
    T. Gerber, M. Dutasta, L. Girin & C. Févotte Professionally-produced music separation guided by covers 2012 Proc. International Society for Music Information Retrieval Conference (ISMIR)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{ismir12b,
      author = {T. Gerber and M. Dutasta and L. Girin and C. Févotte} ,
      title = {Professionally-produced music separation guided by covers},
      booktitle = {Proc. International Society for Music Information Retrieval Conference (ISMIR)},
      address = {Porto, Portugal},
      month = {Oct.},
      year = {2012},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/ismir12b.pdf}
    }
    
    S. Essid & C. Févotte Decomposing the video editing structure of a talk-show using nonnegative matrix factorization 2012 Proc. IEEE International Conference on Image Processing (ICIP)   inproceedings  
    BibTeX:
    
    @inproceedings{icip12,
      author = {S. Essid and C. Févotte} ,
      title = {Decomposing the video editing structure of a talk-show using nonnegative matrix factorization},
      booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)},
      address = {Orlando, Florida},
      month = {Sep.},
      year = {2012}
    }
    
    B. King, C. Févotte & P. Smaragdis Optimal cost function and magnitude power for NMF-based speech separation and music interpolation 2012 Proc. IEEE International Workshop on Machine Learning for Signal Processing (MLSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{mlsp12,
      author = {B. King and C. Févotte and P. Smaragdis} ,
      title = {Optimal cost function and magnitude power for NMF-based speech separation and music interpolation},
      booktitle = {Proc. IEEE International Workshop on Machine Learning for Signal Processing (MLSP)},
      address = {Santander, Spain},
      month = {Sep.},
      year = {2012},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/mlsp12.pdf}
    }
    
    J. Hershey, C. Févotte & J. Le Roux Method for transforming non-stationary signals using a dynamic model 2012   patent [PDF]  
    BibTeX:
    
    @patent{patent2013,
      author = {J. Hershey and C.  Févotte and J. Le Roux} ,
      title = {Method for transforming non-stationary signals using a dynamic model},
      month = {Oct.},
      year = {2012},
      number = {13657077},
      note = {US Patent 13657077, filed.},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/patents/application14.pdf}
    }
    
    L. Oudre, C. Févotte & Y. Grenier Probabilistic template-based chord recognition 2011 IEEE Transactions on Audio, Speech and Language Processing   article [PDF] [DOI] [CODE]  
    Abstract: This paper describes a probabilistic approach to template-based chord
    recognition in music signals. The algorithm only takes chromagram
    data and a user-defined dictionary of chord templates as input data.
    No training or musical information such as key, rhythm or chord transition
    models is required. The chord occurrences are treated as probabilistic
    events, whose probabilities are learned from the song using an Expectation-Maximization
    (EM) algorithm. The adaptative estimation of these probabilities
    (together with an ad-hoc post-processing filtering) has the desirable
    effect of smoothing out spurious chords that would occur in our previous
    baseline work. Our algorithm is compared to various methods that
    entered the Music Information Retrieval Evaluation eXchange (MIREX)
    in 2008 and 2009, using a diverse set of evaluation metrics, some
    of which are new. The systems are tested on two evaluation corpuses;
    the first one is composed of the Beatles catalog (180 pop-rock songs)
    and the other one is constituted of 20 songs from various artists
    and music genres. Results show that our method outperforms state-of-the-art
    chord recognition systems.
    BibTeX:
    
    @article{ieee_taslp_2011a,
      author = {L. Oudre and C. Févotte and Y. Grenier} ,
      title = {Probabilistic template-based chord recognition},
      journal = {IEEE Transactions on Audio, Speech and Language Processing},
      month = {Nov.},
      year = {2011},
      volume = {19},
      number = {8},
      pages = {2249 -- 2259},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/ieee_asl_probachord.pdf},
      doi = {https://doi.org/10.1109/TASL.2010.2098870}
    }
    
    C. Févotte & J. Idier Algorithms for nonnegative matrix factorization with the beta-divergence 2011 Neural Computation   article [PDF] [DOI] [CODE]  
    Abstract: This paper describes algorithms for nonnegative matrix factorization
    (NMF) with the beta-divergence (beta-NMF). The beta-divergence is
    a family of cost functions parametrized by a single shape parameter
    beta that takes the Euclidean distance, the Kullback-Leibler divergence
    and the Itakura-Saito divergence as special cases (beta = 2,1,0,
    respectively). The proposed algorithms are based on a surrogate auxiliary
    function (a local majorization of the criterion function). We first
    describe a majorization-minimization (MM) algorithm that leads to
    multiplicative updates, which differ from standard heuristic multiplicative
    updates by a beta-dependent power exponent. The monotonicity of the
    heuristic algorithm can however be proven for beta in (0,1) using
    the proposed auxiliary function. Then we introduce the concept of
    majorization-equalization (ME) algorithm which produces updates that
    move along constant level sets of the auxiliary function and lead
    to larger steps than MM. Simulations on synthetic and real data illustrate
    the faster convergence of the ME approach. The paper also describes
    how the proposed algorithms can be adapted to two common variants
    of NMF : penalized NMF (i.e., when a penalty function of the factors
    is added to the criterion function) and convex-NMF (when the dictionary
    is assumed to belong to a known subspace).
    BibTeX:
    
    @article{betanmf,
      author = {C. Févotte and J. Idier} ,
      title = {Algorithms for nonnegative matrix factorization with the beta-divergence},
      journal = {Neural Computation},
      month = {Sep.},
      year = {2011},
      volume = {23},
      number = {9},
      pages = {2421--2456},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/neco11.pdf},
      doi = {https://doi.org/10.1162/NECO_a_00168}
    }
    
    L. Oudre, Y. Grenier & C. Févotte Chord recognition by fitting rescaled chroma vectors to chord templates 2011 IEEE Transactions on Audio, Speech and Language Processing   article [PDF] [DOI] [CODE]  
    BibTeX:
    
    @article{ieee_taslp_2011b,
      author = {L. Oudre and Y. Grenier and C. Févotte} ,
      title = {Chord recognition by fitting rescaled chroma vectors to chord templates},
      journal = {IEEE Transactions on Audio, Speech and Language Processing},
      month = {Sep.},
      year = {2011},
      volume = {19},
      number = {7},
      pages = {2222 -- 2233},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/ieee_asl_deterchord.pdf},
      doi = {https://doi.org/10.1109/TASL.2011.2139205}
    }
    
    O. Dikmen & C. Févotte Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation 2011 Advances in Neural Information Processing Systems (NIPS)   inproceedings [PDF] [CODE]  
    Abstract: In this paper we describe a maximum likelihood approach for dictionary
    learning in the multiplicative exponential noise model. This model
    is prevalent in audio signal processing where it underlies a generative
    composite model of the power spectrogram. Maximum joint likelihood
    estimation of the dictionary and expansion coefficients leads to
    a nonnegative matrix factorization problem where the Itakura-Saito
    divergence is used. The optimality of this approach is in question
    because the number of parameters (which include the expansion coefficients)
    grows with the number of observations. In this paper we describe
    a variational procedure for optimization of the marginal likelihood,
    i.e., the likelihood of the dictionary where the activation coefficients
    have been integrated out (given a specific prior). We compare the
    output of both maximum joint likelihood estimation (i.e., standard
    Itakura-Saito NMF) and maximum marginal likelihood estimation (MMLE)
    on real and synthetical datasets. The MMLE approach is shown to embed
    automatic model order selection, akin to automatic relevance determination.
    BibTeX:
    
    @inproceedings{nips11,
      author = {O. Dikmen and C. Févotte} ,
      title = {Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation},
      booktitle = {Advances in Neural Information Processing Systems (NIPS)},
      address = {Granada, Spain},
      month = {Dec.},
      year = {2011},
      pages = {2267--2275},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/nips11.pdf}
    }
    
    V.Y.F. Tan & C. Févotte Automatic relevance determination in nonnegative matrix factorization with the beta-divergence 2011 Proc. NIPS workshop on Sparse Representation and Low-rank Approximation   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{nipswork11,
      author = {V. Y. F. Tan and C. Févotte} ,
      title = {Automatic relevance determination in nonnegative matrix factorization with the beta-divergence},
      booktitle = {Proc. NIPS workshop on Sparse Representation and Low-rank Approximation},
      address = {Sierra Nevada, Spain},
      month = {Dec.},
      year = {2011},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/nips11_workshop.pdf}
    }
    
    A. Lefèvre, F. Bach & C. Févotte Online algorithms for nonnegative matrix factorization with the Itakura-Saito divergence 2011 Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{onlineisnmf,
      author = {A. Lefèvre and F. Bach and C. Févotte} ,
      title = {Online algorithms for nonnegative matrix factorization with the Itakura-Saito divergence},
      booktitle = {Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
      address = {Mohonk, NY},
      month = {Oct.},
      year = {2011},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/waspaa11.pdf}
    }
    
    C. Févotte & J. Idier Algorithmes de factorisation en matrices non-négatives fondée sur la beta-divergence 2011 Proc. 23e colloque GRETSI sur le Traitement du Signal et des Images   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{gretsi11a,
      author = {Févotte, C. and Idier, J.} ,
      title = {Algorithmes de factorisation en matrices non-négatives fondée sur la beta-divergence},
      booktitle = {Proc. 23e colloque GRETSI sur le Traitement du Signal et des Images},
      address = {Bordeaux, France},
      month = {Sep.},
      year = {2011},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/gretsi11a.pdf}
    }
    
    O. Cappé, C. Févotte & D. Rhodes Algorithme EM en ligne simulé pour la factorisation non-négative probabiliste 2011 Proc. 23e colloque GRETSI sur le Traitement du Signal et des Images   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{gretsi11b,
      author = {Cappé, O. and Févotte, C. and Rhodes, D.} ,
      title = {Algorithme EM en ligne simulé pour la factorisation non-négative probabiliste},
      booktitle = {Proc. 23e colloque GRETSI sur le Traitement du Signal et des Images},
      address = {Bordeaux, France},
      month = {Sep.},
      year = {2011},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/gretsi11b.pdf}
    }
    
    A. Lefèvre, F. Bach & C. Févotte Factorisation de matrices structurée en groupes avec la divergence d'Itakura-Saito 2011 Proc. 23e colloque GRETSI sur le Traitement du Signal et des Images   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{gretsi11c,
      author = {Lefèvre, A. and Bach, F. and Févotte, C.} ,
      title = {Factorisation de matrices structurée en groupes avec la divergence d'Itakura-Saito},
      booktitle = {Proc. 23e colloque GRETSI sur le Traitement du Signal et des Images},
      address = {Bordeaux, France},
      month = {Sep.},
      year = {2011},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/gretsi11c.pdf}
    }
    
    C. Févotte, O. Cappé & A.T. Cemgil Efficient Markov chain Monte Carlo inference in composite models with space alternating data augmentation 2011 Proc. IEEE Workshop on Statistical Signal Processing (SSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{ssp11,
      author = {C. Févotte and O. Cappé and A. T. Cemgil} ,
      title = {Efficient Markov chain Monte Carlo inference in composite models with space alternating data augmentation},
      booktitle = {Proc. IEEE Workshop on Statistical Signal Processing (SSP)},
      address = {Nice, France},
      month = {June},
      year = {2011},
      pages = {221 -- 224},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/ssp11.pdf}
    }
    
    C. Févotte Majorization-minimization algorithm for smooth Itakura-Saito nonnegative matrix factorization 2011 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF] [CODE] [DEMO]  
    BibTeX:
    
    @inproceedings{icassp11a,
      author = {C. Févotte} ,
      title = {Majorization-minimization algorithm for smooth Itakura-Saito nonnegative matrix factorization},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Prague, Czech Republic},
      month = {May},
      year = {2011},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp11a.pdf}
    }
    
    O. Dikmen & C. Févotte Maximum marginal likelihood estimation for nonnegative dictionary learning 2011 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF] [CODE]  
    BibTeX:
    
    @inproceedings{icassp11b,
      author = {O. Dikmen and C. Févotte} ,
      title = {Maximum marginal likelihood estimation for nonnegative dictionary learning},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Prague, Czech Republic},
      month = {May},
      year = {2011},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp11b.pdf}
    }
    
    A. Lefèvre, F. Bach & C. Févotte Itakura-Saito nonnegative matrix factorization with group sparsity 2011 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{icassp11c,
      author = {A. Lefèvre and F. Bach and C. Févotte} ,
      title = {Itakura-Saito nonnegative matrix factorization with group sparsity},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Prague, Czech Republic},
      month = {May},
      year = {2011},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp11c.pdf}
    }
    
    A. Ozerov, C. Févotte, R. Blouet & J.-L. Durrieu Multichannel nonnegative tensor factorization with structured constraints for user-guided audio source separation 2011 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{icassp11d,
      author = {A. Ozerov and C. Févotte and R. Blouet and J.-L. Durrieu} ,
      title = {Multichannel nonnegative tensor factorization with structured constraints for user-guided audio source separation},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Prague, Czech Republic},
      month = {May},
      year = {2011},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp11d.pdf}
    }
    
    A. Ozerov, C. Févotte & R. Blouet Automatic source separation via joint use of segmental information and spatial diversity 2011   patent [PDF]  
    BibTeX:
    
    @patent{patent11,
      author = {A. Ozerov and C. Févotte and R. Blouet} ,
      title = {Automatic source separation via joint use of segmental information and spatial diversity},
      month = {Feb.},
      year = {2011},
      number = {13021692},
      note = {US Patent 13021692, filed.},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/patents/application11.pdf}
    }
    
    J.-L. Durrieu, G. Richard, B. David & C. Févotte Source/Filter model for unsupervised main melody extraction from polyphonic audio signals 2010 IEEE Transactions on Audio, Speech and Language Processing   article [PDF] [DOI]  
    Abstract: Extracting the main melody from a polyphonic music recording seems
    natural even to untrained human listeners. To a certain extent it
    is related to the concept of source separation, with the human ability
    of focusing on a specific source in order to extract relevant information.
    In this paper, we propose a new approach for the estimation and extraction
    of the main melody (and in particular the leading vocal part) from
    polyphonic audio signals. To that aim, we propose a new signal model
    where the leading vocal part is explicitly represented by a specific
    source/filter model. The proposed representation is investigated
    in the framework of two statistical models: a Gaussian Scaled Mixture
    Model (GSMM) and an extended Instantaneous Mixture Model (IMM). For
    both models, the estimation of the different parameters is done within
    a maximum-likelihood framework adapted from single-channel source
    separation techniques. The desired sequence of fundamental frequencies
    is then inferred from the estimated parameters. The results obtained
    in a recent evaluation campaign (MIREX08) show that the proposed
    approaches are very promising and reach state-of-the-art performances
    on all test sets.
    BibTeX:
    
    @article{durr10,
      author = {J.-L. Durrieu and G. Richard and B. David and C. Févotte} ,
      title = {Source/Filter model for unsupervised main melody extraction from polyphonic audio signals},
      journal = {IEEE Transactions on Audio, Speech and Language Processing},
      month = {Mar.},
      year = {2010},
      volume = {18},
      number = {3},
      pages = {564--575},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/ieee_asl_voice_extrac.pdf},
      doi = {https://doi.org/10.1109/TASL.2010.2041114}
    }
    
    A. Ozerov & C. Févotte Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation 2010 IEEE Transactions on Audio, Speech and Language Processing   article [PDF] [DOI] [CODE] [DEMO]  
    Abstract: We consider inference in a general data-driven object-based model
    of multichannel audio data, assumed generated as a possibly underdetermined
    convolutive mixture of source signals. We work in the short-time
    Fourier transform (STFT) domain, where convolution is routinely approximated
    as linear instantaneous mixing in each frequency band. Each source
    STFT is given a model inspired from nonnegative matrix factorization
    (NMF) with the Itakura-Saito divergence, which underlies a statistical
    model of superimposed Gaussian components. We address estimation
    of the mixing and source parameters using two methods. The first
    one consists of maximizing the exact joint likelihood of the multichannel
    data using an expectation-maximization (EM) algorithm. The second
    method consists of maximizing the sum of individual likelihoods of
    all channels using a multiplicative update algorithm inspired from
    NMF methodology. Our decomposition algorithms are applied to stereo
    audio source separation in various settings, covering blind and supervised
    separation, music and speech sources, synthetic instantaneous and
    convolutive mixtures, as well as professionally produced music recordings.
    Our EM method produces competitive results with respect to state-of-the-art
    as illustrated on two tasks from the international Signal Separation
    Evaluation Campaign (SiSEC 2008).
    BibTeX:
    
    @article{ieee_asl10,
      author = {A. Ozerov and C. Févotte} ,
      title = {Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation},
      journal = {IEEE Transactions on Audio, Speech and Language Processing},
      month = {Mar.},
      year = {2010},
      volume = {18},
      number = {3},
      pages = {550--563},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/ieee_asl_multinmf.pdf},
      doi = {https://doi.org/10.1109/TASL.2009.2031510}
    }
    
    C. Févotte Itakura-Saito nonnegative factorizations of the power spectrogram for music signal decomposition 2010 Machine Audition: Principles, Algorithms and Systems   incollection [PDF] [DOI] [DEMO]  
    Abstract: Nonnegative matrix factorization (NMF) is a popular linear regression
    technique in the fields of machine learning and signal/image processing.
    Much research about this topic has been driven by applications in
    audio. NMF has been for example applied with success to automatic
    music transcription and audio source separation, where the data is
    usually taken as the magnitude spectrogram of the sound signal, and
    the Euclidean distance or Kullback-Leibler divergence are used as
    measures of fit between the original spectrogram and its approximate
    factorization. In this chapter we give evidence of the relevance
    of considering factorization of the power spectrogram, with the Itakura-Saito
    (IS) divergence. Indeed, IS-NMF is shown to be connected to maximum
    likelihood inference of variance parameters in a well-defined statistical
    model of superimposed Gaussian components and this model is in turn
    shown to be well suited to audio. Furthermore, the statistical setting
    opens doors to Bayesian approaches and to a variety of computational
    inference techniques. We discuss in particular model order selection
    strategies and Markov regularization of the activation matrix, to
    account for time-persistence in audio. This chapter also discusses
    extensions of NMF to the multichannel case, in both instantaneous
    or convolutive recordings, possibly underdetermined. We present in
    particular audio source separation results of a real stereo musical
    excerpt.
    BibTeX:
    
    @incollection{wangbook,
      author = {C. Févotte} ,
      title = {Itakura-Saito nonnegative factorizations of the power spectrogram for music signal decomposition},
      booktitle = {Machine Audition: Principles, Algorithms and Systems},
      editor = {Wenwu Wang},
      publisher = {IGI Global Press},
      month = {Aug.},
      year = {2010},
      chapter = {11},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/chapters/isnmf.pdf},
      doi = {https://doi.org/10.4018/978-1-61520-919-4}
    }
    
    L. Oudre, C. Févotte & Y. Grenier Probabilistic framework for template-based chord recognition 2010 Proc. IEEE International Workshop on Multimedia Signal Processing (MMSP)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{mmsp10,
      author = {L. Oudre and C. Févotte and Y. Grenier} ,
      title = {Probabilistic framework for template-based chord recognition},
      booktitle = {Proc. IEEE International Workshop on Multimedia Signal Processing (MMSP)},
      address = {St-Malo, France},
      month = {Oct.},
      year = {2010},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/mmsp10.pdf}
    }
    
    H. Lantéri, C. Theys, C. Richard & C. Févotte Split gradient method for nonnegative matrix factorization 2010 Proc. 18th European Signal Processing Conference (EUSIPCO)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{eusipco10,
      author = {H. Lantéri and C. Theys and C. Richard and C. Févotte} ,
      title = {Split gradient method for nonnegative matrix factorization},
      booktitle = {Proc. 18th European Signal Processing Conference (EUSIPCO)},
      address = {Aalborg, Denmark},
      month = {Aug.},
      year = {2010},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/eusipco10.pdf}
    }
    
    C. Févotte & A. Ozerov Notes on nonnegative tensor factorization of the spectrogram for audio source separation : statistical insights and towards self-clustering of the spatial cues 2010 Proc. 7th International Symposium on Computer Music Modeling and Retrieval (CMMR)   inproceedings [PDF] [CODE] [DEMO]  
    Abstract: Nonnegative tensor factorization (NTF) of multichannel spectrograms
    under PARAFAC structure has recently been proposed by Fitzgerald
    et al as a mean of performing blind source separation (BSS) of multichannel
    audio data. In this paper we investigate the statistical source models
    implied by this approach. We show that it implicitly assumes a nonpoint-source
    model contrasting with usual BSS assumptions and we clarify the links
    between the measure of fit chosen for the NTF and the implied statistical
    distribution of the sources. While the original approach of Fitzgeral
    et al requires a posterior clustering of the spatial cues to group
    the NTF components into sources, we discuss means of performing the
    clustering within the factorization. In the results section we test
    the impact of the simplifying nonpoint-source assumption on underdetermined
    linear instantaneous mixtures of musical sources and discuss the
    limits of the approach for such mixtures.
    BibTeX:
    
    @inproceedings{cmmr10,
      author = {C. Févotte and A. Ozerov} ,
      title = {Notes on nonnegative tensor factorization of the spectrogram for audio source separation : statistical insights and towards self-clustering of the spatial cues},
      booktitle = {Proc. 7th International Symposium on Computer Music Modeling and Retrieval (CMMR)},
      editor = {S. Ystad and M.  Aramaki and R. Kronland-Martinet and K. Jensen},
      publisher = {Springer},
      address = {Málaga, Spain, 2010.},
      month = {June},
      year = {2010},
      volume = {6684},
      pages = {102-115},
      note = {Long paper.},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/cmmr10.pdf}
    }
    
    C. Févotte, N. Bertin & J.-L. Durrieu Nonnegative matrix factorization with the Itakura-Saito divergence. With application to music analysis 2009 Neural Computation   article [PDF] [DOI] [CODE] [DEMO]  
    Abstract: This letter presents theoretical, algorithmic, and experimental results
    about nonnegative matrix factorization (NMF) with the Itakura-Saito
    (IS) divergence. We describe how IS-NMF is underlaid by a well-defined
    statistical model of superimposed gaussian components and is equivalent
    to maximum likelihood estimation of variance parameters. This setting
    can accommodate regularization constraints on the factors through
    Bayesian priors. In particular, inverse-gamma and gamma Markov chain
    priors are considered in this work. Estimation can be carried out
    using a space-alternating generalized expectation-maximization (SAGE)
    algorithm; this leads to a novel type of NMF algorithm, whose convergence
    to a stationary point of the IS cost function is guaranteed.


    We also discuss the links between the IS divergence and other cost
    functions used in NMF, in particular, the Euclidean distance and
    the generalized Kullback-Leibler (KL) divergence. As such, we describe
    how IS-NMF can also be performed using a gradient multiplicative
    algorithm (a standard algorithm structure in NMF) whose convergence
    is observed in practice, though not proven.


    Finally, we report a furnished experimental comparative study of Euclidean-NMF,
    KL-NMF, and IS-NMF algorithms applied to the power spectrogram of
    a short piano sequence recorded in real conditions, with various
    initializations and model orders. Then we show how IS-NMF can successfully
    be employed for denoising and upmix (mono to stereo conversion) of
    an original piece of early jazz music. These experiments indicate
    that IS-NMF correctly captures the semantics of audio and is better
    suited to the representation of music signals than NMF with the usual
    Euclidean and KL costs.
    BibTeX:
    
    @article{neco09,
      author = {C. Févotte and N. Bertin and J.-L. Durrieu} ,
      title = {Nonnegative matrix factorization with the Itakura-Saito divergence. With application to music analysis},
      journal = {Neural Computation},
      month = {Mar.},
      year = {2009},
      volume = {21},
      number = {3},
      pages = {793-830},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/neco09_is-nmf.pdf},
      doi = {https://doi.org/10.1162/neco.2008.04-08-771}
    }
    
    L. Oudre, Y. Grenier & C. Févotte Template-based chord recognition : influence of the chord types 2009 Proc. International Society for Music Information Retrieval Conference (ISMIR)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{ismir09,
      author = {L. Oudre and Y. Grenier and C. Févotte} ,
      title = {Template-based chord recognition : influence of the chord types},
      booktitle = {Proc. International Society for Music Information Retrieval Conference (ISMIR)},
      address = {Kobe, Japan},
      month = {Oct.},
      year = {2009},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/ismir09.pdf}
    }
    
    A. Ozerov, C. Févotte & M. Charbit Factorial scaled hidden Markov model for polyphonic audio representation and source separation 2009 Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)   inproceedings [PDF] [DEMO]  
    BibTeX:
    
    @inproceedings{waspaa09-1,
      author = {A. Ozerov and C. Févotte and M. Charbit} ,
      title = {Factorial scaled hidden Markov model for polyphonic audio representation and source separation},
      booktitle = {Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
      address = {Mohonk, NY, USA},
      month = {Oct.},
      year = {2009},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/waspaa09a.pdf}
    }
    
    L. Oudre, Y. Grenier & C. Févotte Chord recognition using measures of fit, chord templates and filtering methods 2009 Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{waspaa09b,
      author = {L. Oudre and Y. Grenier and C. Févotte} ,
      title = {Chord recognition using measures of fit, chord templates and filtering methods},
      booktitle = {Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
      address = {Mohonk, NY},
      month = {Oct.},
      year = {2009},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/waspaa09b.pdf}
    }
    
    C. Févotte & A.T. Cemgil Nonnegative matrix factorisations as probabilistic inference in composite models 2009 Proc. 17th European Signal Processing Conference (EUSIPCO)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{eusipco09,
      author = {C. Févotte and A. T. Cemgil} ,
      title = {Nonnegative matrix factorisations as probabilistic inference in composite models},
      booktitle = {Proc. 17th European Signal Processing Conference (EUSIPCO)},
      address = {Glasgow, Scotland},
      month = {Aug.},
      year = {2009},
      pages = {1913--1917},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/eusipco09a.pdf}
    }
    
    J.-L. Durrieu, A. Ozerov, C. Févotte, G. Richard & B. David Main instrument separation from stereophonic audio signals using a source/filter models 2009 Proc. 17th European Signal Processing Conference (EUSIPCO)   inproceedings [PDF] [DEMO]  
    BibTeX:
    
    @inproceedings{eusipco09b,
      author = {J.-L. Durrieu and A. Ozerov and C. Févotte and G. Richard and B. David} ,
      title = {Main instrument separation from stereophonic audio signals using a source/filter models},
      booktitle = {Proc. 17th European Signal Processing Conference (EUSIPCO)},
      address = {Glasgow, Scotland},
      month = {Aug.},
      year = {2009},
      pages = {15--19},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/eusipco09b.pdf}
    }
    
    A. Ozerov & C. Févotte Multichannel nonnegative matrix factorization in convolutive mixtures. With application to blind audio source separation 2009 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF] [DOI] [DEMO]  
    BibTeX:
    
    @inproceedings{icassp09a,
      author = {A. Ozerov and C. Févotte} ,
      title = {Multichannel nonnegative matrix factorization in convolutive mixtures. With application to blind audio source separation},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Taipei, Taiwan},
      month = {Apr.},
      year = {2009},
      pages = {3137-3140},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp09a.pdf},
      doi = {https://doi.org/10.1109/ICASSP.2009.4960289}
    }
    
    N. Bertin, C. Févotte & R. Badeau A tempering approach for Itakura-Saito non-negative matrix factorization. With application to music transcription 2009 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF] [DOI]  
    BibTeX:
    
    @inproceedings{icassp09b,
      author = {N. Bertin and C. Févotte and R. Badeau} ,
      title = {A tempering approach for Itakura-Saito non-negative matrix factorization. With application to music transcription},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Taipei, Taiwan},
      month = {Apr.},
      year = {2009},
      pages = {1545 --1548},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp09b.pdf},
      doi = {https://doi.org/10.1109/ICASSP.2009.4959891}
    }
    
    V.Y.F. Tan & C. Févotte Automatic relevance determination in nonnegative matrix factorization 2009 Proc. Workshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS)   inproceedings [PDF] [CODE]  
    BibTeX:
    
    @inproceedings{spars09,
      author = {V. Y. F. Tan and C. Févotte} ,
      title = {Automatic relevance determination in nonnegative matrix factorization},
      booktitle = {Proc. Workshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS)},
      address = {St-Malo, France},
      month = {Apr.},
      year = {2009},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/spars09.pdf}
    }
    
    C. Févotte, B. Torrésani, L. Daudet & S.J. Godsill Sparse linear regression with structured priors and application to denoising of musical audio 2008 IEEE Transactions on Audio, Speech and Language Processing   article [PDF] [DOI] [CODE] [DEMO]  
    Abstract: We describe in this paper an audio denoising technique based on sparse
    linear regression with structured priors. The noisy signal is decomposed
    as a linear combination of atoms belonging to two modified discrete
    cosine transform (MDCT) bases, plus a residual part containing the
    noise. One MDCT basis has a long time resolution, and thus high frequency
    resolution, and is aimed at modeling tonal parts of the signal, while
    the other MDCT basis has short time resolution and is aimed at modeling
    transient parts (such as attacks of notes). The problem is formulated
    within a Bayesian setting. Conditional upon an indicator variable
    which is either 0 or 1, one expansion coefficient is set to zero
    or given a hierarchical prior. Structured priors are employed for
    the indicator variables; using two types of Markov chains, persistency
    along the time axis is favored for expansion coefficients of the
    tonal layer, while persistency along the frequency axis is favored
    for the expansion coefficients of the transient layer. Inference
    about the denoised signal and model parameters is performed using
    a Gibbs sampler, a standard Markov chain Monte Carlo (MCMC) sampling
    technique. We present results for denoising of a short glockenspiel
    excerpt and a long polyphonic music excerpt. Our approach is compared
    with unstructured sparse regression and with structured sparse regression
    in a single resolution MDCT basis (no transient layer). The results
    show that better denoising is obtained, both from signal-to-noise
    ratio measurements and from subjective criteria, when both a transient
    and tonal layer are used, in conjunction with our proposed structured
    prior framework.
    BibTeX:
    
    @article{ieee_asl07b,
      author = {C. Févotte and B. Torrésani and L. Daudet and S. J. Godsill} ,
      title = {Sparse linear regression with structured priors and application to denoising of musical audio},
      journal = {IEEE Transactions on Audio, Speech and Language Processing},
      month = {Jan.},
      year = {2008},
      volume = {16},
      number = {1},
      pages = {174--185},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/ieee_asl_sparsereg_struc.pdf},
      doi = {https://doi.org/10.1109/TASL.2007.909290}
    }
    
    R. Blouet, G. Rapaport, I. Cohen & C. Févotte Evaluation of several strategies for single sensor speech/music separation 2008 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF] [DOI]  
    BibTeX:
    
    @inproceedings{icassp08,
      author = {R. Blouet and G. Rapaport and I. Cohen and C. Févotte} ,
      title = {Evaluation of several strategies for single sensor speech/music separation},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Las Vegas, USA},
      month = {Apr.},
      year = {2008},
      pages = {37-40},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp08.pdf},
      doi = {https://doi.org/10.1109/ICASSP.2008.4517540}
    }
    
    P. Aimé & C. Févotte La simplification administrative de la gestion des unités de recherche 2008 Rapport de l'Inspection Générale de l'Éducation Nationale et de la Recherche (IGAENR), no 2008-089   misc [PDF]  
    BibTeX:
    
    @misc{igaenr09,
      author = {P. Aimé and C. Févotte} ,
      title = {La simplification administrative de la gestion des unités de recherche},
      month = {Oct.},
      year = {2008},
      note = {http://media.enseignementsup-recherche.gouv.fr/file/Concours_2008/22/3/2008-089simplification_44223.pdf},
      url = {http://media.enseignementsup-recherche.gouv.fr/file/Concours_2008/22/3/2008-089simplification_44223.pdf}
    }
    
    A.T. Cemgil, C. Févotte & S.J. Godsill Variational and stochastic inference for Bayesian Source Separation 2007 Digital Signal Processing   article [PDF]  
    Abstract: We tackle the general linear instantaneous model (possibly underdetermined
    and noisy) where we model the source prior with a Student t distribution.
    The conjugate-exponential characterisation of the t distribution
    as an infinite mixture of scaled Gaussians enables us to do efficient
    inference.We study two well-known inference methods, Gibbs sampler
    and variational Bayes for Bayesian source separation. We derive both
    techniques as local message passing algorithms to highlight their
    algorithmic similarities and to contrast their different convergence
    characteristics and computational requirements. Our simulation results
    suggest that typical posterior distributions in source separation
    have multiple local maxima. Therefore we propose a hybrid approach
    where we explore the state space with a Gibbs sampler and then switch
    to a deterministic algorithm. This approach seems to be able to combine
    the speed of the variational approach with the robustness of the
    Gibbs sampler.
    BibTeX:
    
    @article{dsp07,
      author = {A. T. Cemgil and C. Févotte and S. J. Godsill} ,
      title = {Variational and stochastic inference for Bayesian Source Separation},
      journal = {Digital Signal Processing},
      month = {Sep.},
      year = {2007},
      volume = {17},
      number = {5},
      pages = {891--913},
      note = {Special issue Bayesian source separation, ed. E. E. Kuruoglu and K. H. Knuth},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/elsevier_dsp_variational.pdf}
    }
    
    C. Févotte Bayesian audio source separation 2007 Blind speech separation   incollection [PDF] [DOI] [CODE] [DEMO]  
    Abstract: In this chapter we describe a Bayesian approach to audio source separation.
    The approach relies on probabilistic modeling of sound sources as
    (sparse) linear combinations of atoms from

    a dictionary and Markov chain Monte Carlo (MCMC) inference. Several
    prior distributions are considered for the source expansion coefficients.
    We first consider independent and iden-

    tically distributed (iid) general priors with two choices of distributions.
    The first one is the Student t, which is a good model for sparsity
    when the shape parameter has a low value. The

    second one is a hierarchical mixture distribution; conditionally upon
    an indicator variable, one coefficient is either set to zero or given
    a normal distribution, whose variance is in turn

    given an inverted-Gamma distribution. Then, we consider more audio-specific
    models where both the identically distributed and independently distributed
    assumptions are lifted. Using a

    Modified Discrete Cosine Transform (MDCT) dictionary, a time-frequency
    orthonormal basis, we describe frequency-dependent structured priors
    which explicitly model the harmonic struc-

    ture of sound, using a Markov hierarchical modeling of the expansion
    coefficients. Separation results are given for a stereophonic recording
    of 3 sources.
    BibTeX:
    
    @incollection{makinobook,
      author = {C. Févotte} ,
      title = {Bayesian audio source separation},
      booktitle = {Blind speech separation},
      editor = {S. Makino and T.-W. Lee and H. Sawada},
      publisher = {Springer},
      month = {Sep.},
      year = {2007},
      chapter = {11},
      pages = {305--335},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/chapters/bass.pdf},
      doi = {http://www.springer.com/engineering/signals/book/978-1-4020-6478-4}
    }
    
    S.J. Godsill, A.T. Cemgil, C. Févotte & P.J. Wolfe Bayesian computational methods for sparse audio and music processing 2007 Proc. 15th European Signal Processing Conference (EUSIPCO)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{eusipco07,
      author = {S. J. Godsill and A. T. Cemgil and C. Févotte and P. J. Wolfe} ,
      title = {Bayesian computational methods for sparse audio and music processing},
      booktitle = {Proc. 15th European Signal Processing Conference (EUSIPCO)},
      address = {Poznaʼn, Poland},
      month = {Sep.},
      year = {2007},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/eusipco07.pdf}
    }
    
    C. Févotte & F. Theis Pivot selection strategies in Jacobi joint block-diagonalization 2007 Proc. 7th International Conference on Independent Component Analysis and Signal Separation (ICA)   inproceedings [PDF] [CODE]  
    BibTeX:
    
    @inproceedings{ica07,
      author = {C. Févotte and F. Theis} ,
      title = {Pivot selection strategies in Jacobi joint block-diagonalization},
      booktitle = {Proc. 7th International Conference on Independent Component Analysis and Signal Separation (ICA)},
      address = {London, UK},
      month = {Sep.},
      year = {2007},
      pages = {177--187},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/ica07.pdf}
    }
    
    C. Févotte & F. Theis Orthonormal approximate joint block-diagonalization 2007 GET/Télécom Paris   techreport [PDF] [CODE]  
    BibTeX:
    
    @techreport{simax06,
      author = {C. Févotte and F. Theis} ,
      title = {Orthonormal approximate joint block-diagonalization},
      institution = {GET/Télécom Paris},
      month = {Apr.},
      year = {2007},
      number = {2007D007},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/techreps/techrep07.pdf}
    }
    
    C. Févotte & S.J. Godsill A Bayesian approach to blind separation of sparse sources 2006 IEEE Transactions on Audio, Speech and Language Processing   article [PDF] [DOI] [CODE] [DEMO]  
    Abstract: In this paper, we discuss the evaluation of blind audio source separation
    (BASS) algorithms. Depending on the exact application, different
    distortions can be allowed between an estimated source and the wanted
    true source. We consider four different sets of such allowed distortions,
    from time-invariant gains to time-varying filters. In each case,
    we decompose the estimated source into a true source part plus error
    terms corresponding to interferences, additive noise, and algorithmic
    artifacts. Then, we derive a global performance measure using an
    energy ratio, plus a separate performance measure for each error
    term. These measures are computed and discussed on the results of
    several BASS problems with various difficulty levels
    BibTeX:
    
    @article{ieee_asl07,
      author = {C. Févotte and S. J. Godsill} ,
      title = {A Bayesian approach to blind separation of sparse sources},
      journal = {IEEE Transactions on Audio, Speech and Language Processing},
      month = {Nov.},
      year = {2006},
      volume = {14},
      number = {6},
      pages = {2174--2188},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/ieee_asl_sparsebss.pdf},
      doi = {https://doi.org/10.1109/TSA.2005.858523}
    }
    
    E. Vincent, R. Gribonval & C. Févotte Performance measurement in blind audio source separation 2006 IEEE Transactions on Audio, Speech and Language Processing   article [PDF] [DOI] [CODE] [DEMO]  
    Abstract: In this paper, we discuss the evaluation of blind audio source separation
    (BASS) algorithms. Depending on the exact application, different
    distortions can be allowed between an estimated source and the wanted
    true source. We consider four different sets of such allowed distortions,
    from time-invariant gains to time-varying filters. In each case,
    we decompose the estimated source into a true source part plus error
    terms corresponding to interferences, additive noise, and algorithmic
    artifacts. Then, we derive a global performance measure using an
    energy ratio, plus a separate performance measure for each error
    term. These measures are computed and discussed on the results of
    several BASS problems with various difficulty levels
    BibTeX:
    
    @article{ieee_asl06,
      author = {E. Vincent and R. Gribonval and C. Févotte} ,
      title = {Performance measurement in blind audio source separation},
      journal = {IEEE Transactions on Audio, Speech and Language Processing},
      month = {Jul.},
      year = {2006},
      volume = {14},
      number = {4},
      pages = {1462--1469},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/ieee_asl_bsseval.pdf},
      doi = {https://doi.org/10.1109/TSA.2005.858005}
    }
    
    C. Févotte & S.J. Godsill Sparse linear regression in unions of bases via Bayesian variable selection 2006 IEEE Signal Processing Letters   article [PDF] [DOI] [DEMO]  
    Abstract: In this letter, we propose an approach for sparse linear regression
    in unions of bases inspired by Bayesian variable selection. Conditionally
    upon an indicator variable that is 0 or 1, one expansion coefficient
    of the signal corresponding to one atom of the dictionary is either
    set to zero or given a Student t prior. A Gibbs sampler (a standard
    Markov chain Monte Carlo technique) is used to sample from the posterior
    distribution of the indicator variables, the expansion coefficients
    (corresponding to nonzero indicator variables), the hyperparameters
    of the Student t priors, and the variance of the residual signal.
    The structure of the dictionary, assumed to be a union of bases,
    allows for alternate sampling of the indicator variables and the
    expansion coefficients from each basis and avoids any large matrix
    inversion. Our method is applied to the denoising problem of a piano
    sequence, using a dual-resolution union of two modified discrete
    cosine transform bases
    BibTeX:
    
    @article{ieee_spl06,
      author = {C. Févotte and S. J. Godsill} ,
      title = {Sparse linear regression in unions of bases via Bayesian variable selection},
      journal = {IEEE Signal Processing Letters},
      month = {Jul.},
      year = {2006},
      volume = {13},
      number = {7},
      pages = {441--444},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/ieee_spl_sparsereg.pdf},
      doi = {https://doi.org/10.1109/LSP.2006.873139}
    }
    
    C. Févotte Bayesian blind separation of audio mixtures with structured priors 2006 Proc. 14th European Signal Processing Conference (EUSIPCO)   inproceedings [PDF] [DEMO]  
    BibTeX:
    
    @inproceedings{eusipco06,
      author = {C. Févotte} ,
      title = {Bayesian blind separation of audio mixtures with structured priors},
      booktitle = {Proc. 14th European Signal Processing Conference (EUSIPCO)},
      address = {Florence, Italy},
      month = {Sep.},
      year = {2006},
      note = {Special session Undetermined sparse audio source separation (invited paper)},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/eusipco06.pdf}
    }
    
    L. Benaroya, R. Blouet, C. Févotte & I. Cohen Single sensor source separation using multiple-window STFT representation 2006 Proc. International Workshop on Acoustic Echo and Noise Control (IWAENC)   inproceedings [PDF] [DEMO]  
    BibTeX:
    
    @inproceedings{iwaenc06,
      author = {L. Benaroya and R. Blouet and C Févotte and I. Cohen} ,
      title = {Single sensor source separation using multiple-window STFT representation},
      booktitle = {Proc. International Workshop on Acoustic Echo and Noise Control (IWAENC)},
      address = {Paris, France},
      month = {Sep.},
      year = {2006},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/iwaenc06.pdf}
    }
    
    F. Desobry & C. Févotte Kernel PCA based estimation of the mixing matrix in linear instantaneous mixtures of sparse sources 2006 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF] [DOI]  
    BibTeX:
    
    @inproceedings{icassp06-1,
      author = {F. Desobry and C. Févotte} ,
      title = {Kernel PCA based estimation of the mixing matrix in linear instantaneous mixtures of sparse sources},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Toulouse, France},
      month = {May},
      year = {2006},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp06a.pdf},
      doi = {https://doi.org/10.1109/ICASSP.2006.1661364}
    }
    
    C. Févotte, L. Daudet, S.J. Godsill & B. Torrésani Sparse regression with structured priors: application to audio denoising 2006 Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   inproceedings [PDF] [DOI] [DEMO]  
    BibTeX:
    
    @inproceedings{icassp06-2,
      author = {C. Févotte and L. Daudet and S. J. Godsill and B. Torrésani} ,
      title = {Sparse regression with structured priors: application to audio denoising},
      booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      address = {Toulouse, France},
      month = {May},
      year = {2006},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/icassp06b.pdf},
      doi = {https://doi.org/10.1109/ICASSP.2006.1660589}
    }
    
    C. Févotte & S.J. Godsill Blind separation of sparse sources using Jeffrey's inverse prior and the EM algorithm 2006 Proc. 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA)   inproceedings [PDF] [DEMO]  
    BibTeX:
    
    @inproceedings{ica06,
      author = {C. Févotte and S. J. Godsill} ,
      title = {Blind separation of sparse sources using Jeffrey's inverse prior and the EM algorithm},
      booktitle = {Proc. 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA)},
      address = {Charleston, SC, USA},
      month = {Mar.},
      year = {2006},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/ica06.pdf}
    }
    
    V.Y.F. Tan & C. Févotte A study of the effect of source sparsity for various transforms on blind audio source separation performance 2005 Proc. Workshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS)   inproceedings [PDF] [DEMO]  
    BibTeX:
    
    @inproceedings{spars05,
      author = {V. Y. F. Tan and C. Févotte} ,
      title = {A study of the effect of source sparsity for various transforms on blind audio source separation performance},
      booktitle = {Proc. Workshop on Signal Processing with Adaptative Sparse Structured Representations (SPARS)},
      address = {Rennes, France},
      month = {Nov.},
      year = {2005},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/spars05.pdf}
    }
    
    C. Févotte & J.-F. Cardoso Maximum likelihood approach for blind audio source separation using time-frequency Gaussian models 2005 Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)   inproceedings [PDF] [DOI] [DEMO]  
    BibTeX:
    
    @inproceedings{waspaa05-1,
      author = {C. Févotte and J.-F. Cardoso} ,
      title = {Maximum likelihood approach for blind audio source separation using time-frequency Gaussian models},
      booktitle = {Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
      address = {Mohonk, NY, USA},
      month = {Oct.},
      year = {2005},
      pages = {78-81},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/waspaa05a.pdf},
      doi = {https://doi.org/10.1109/ASPAA.2005.1540173}
    }
    
    C. Févotte & S.J. Godsill A Bayesian approach to time-frequency based blind source separation 2005 Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)   inproceedings [PDF] [DEMO]  
    BibTeX:
    
    @inproceedings{waspaa05-2,
      author = {C. Févotte and S. J. Godsill} ,
      title = {A Bayesian approach to time-frequency based blind source separation},
      booktitle = {Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
      address = {Mohonk, NY, USA},
      month = {Oct.},
      year = {2005},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/waspaa05b.pdf}
    }
    
    A.T. Cemgil, C. Févotte & S.J. Godsill Blind separation of sparse sources using variational EM 2005 Proc. 13th European Signal Processing Conference (EUSIPCO)   inproceedings [PDF] [DEMO]  
    BibTeX:
    
    @inproceedings{eusipco05,
      author = {A. T. Cemgil and C. Févotte and S. J. Godsill} ,
      title = {Blind separation of sparse sources using variational EM},
      booktitle = {Proc. 13th European Signal Processing Conference (EUSIPCO)},
      address = {Antalya, Turkey},
      month = {Sep.},
      year = {2005},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/eusipco05.pdf}
    }
    
    C. Févotte, R. Gribonval & E. Vincent BSS-EVAL Toolbox User Guide : Revision 2.0 2005 IRISA   techreport [PDF]  
    BibTeX:
    
    @techreport{tr05,
      author = {C. Févotte and R. Gribonval and E. Vincent} ,
      title = {BSS-EVAL Toolbox User Guide : Revision 2.0},
      institution = {IRISA},
      month = {Apr.},
      year = {2005},
      number = {1706},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/techreps/BSSEVAL2userguide.pdf}
    }
    
    D. Farina, C. Févotte, C. Doncarli & R. Merletti Blind separation of linear instantaneous mixtures of non-stationary surface myoelectric signals 2004 IEEE Transactions on Biomedical Engineering   article [PDF] [DOI]  
    Abstract: Electromyographic (EMG) recordings detected over the skin may be mixtures
    of signals generated by different active muscles due to the phenomena
    related to volume conduction. Separation of the sources is necessary
    when single muscle activity has to be detected. Signals generated
    by different muscles may be considered uncorrelated but in general
    overlap in time and frequency. Under certain assumptions, mixtures
    of surface EMG signals can be considered as linear instantaneous
    but no a priori information about the mixing matrix is available
    when different muscles are active. In this study, we applied blind
    source separation (BSS) methods to separate the signals generated
    by two active muscles during a force-varying task. As the signals
    are non stationary, an algorithm based on spatial time-frequency
    distributions was applied on simulated and experimental EMG signals.
    The experimental signals were collected from the flexor carpi radialis
    and the pronator teres muscles which could be activated selectively
    for wrist flexion and rotation, respectively. From the simulations,
    correlation coefficients between the reference and reconstructed
    sources were higher than 0.85 for signals largely overlapping both
    in time and frequency and for signal-to-noise ratios as low as 5
    dB. The Choi-Williams and Bessel kernels, in this case, performed
    better than the Wigner-Ville one. Moreover, the selection of time-frequency
    points for the procedure of joint diagonalization used in the BSS
    algorithm significantly influenced the results. For the experimental
    signals, the interference of the other source in each reconstructed
    source was significantly attenuated by the application of the BSS
    method. The ratio between root-mean-square values of the signals
    from the two sources detected over one of the muscles increased from
    (mean +/- standard deviation) 2.33 +/- 1.04 to 4.51 +/- 1.37 and
    from 1.55 +/- 0.46 to 2.72 +/- 0.65 for wrist flexion and rotation,
    respectively. This increment was statistically significant. It was
    concluded that the BSS approach applied is promising for the separation
    of surface EMG signals, with applications ranging from muscle assessment
    to detection of muscle activation intervals, and to the control of
    myoelectric prostheses.
    BibTeX:
    
    @article{ieee_biomed04,
      author = {D. Farina and C. Févotte and C. Doncarli and R. Merletti} ,
      title = {Blind separation of linear instantaneous mixtures of non-stationary surface myoelectric signals},
      journal = {IEEE Transactions on Biomedical Engineering},
      month = {Sep.},
      year = {2004},
      volume = {51},
      number = {9},
      pages = {1555 --1567},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/ieee_be_surfaceemgs.pdf},
      doi = {https://doi.org/10.1109/TBME.2004.828048}
    }
    
    C. Févotte & C. Doncarli Two contributions to blind source separation using time-frequency distributions 2004 IEEE Signal Processing Letters   article [PDF] [DOI] [CODE]  
    Abstract: We present two improvements/extensions of a previous deterministic
    blind source separation (BSS) technique, by Belouchrani and Amin,
    that involves joint-diagonalization of a set of Cohen's class spatial
    time-frequency distributions. The first contribution concerns the
    extension of the BSS technique to the stochastic case using spatial
    Wigner-Ville spectrum. Then, we show that Belouchrani and Amin's
    technique can be interpreted as a practical implementation of the
    general equations we provide in the stochastic case. The second contribution
    is a new criterion aimed at selecting more efficiently the time-frequency
    locations where the spatial matrices should be joint-diagonalized,
    introducing single autoterms selection. Simulation results on stochastic
    time-varying autoregressive moving average (TVARMA) signals demonstrate
    the improved efficiency of the method.
    BibTeX:
    
    @article{ieee_spl04,
      author = {C. Févotte and C. Doncarli} ,
      title = {Two contributions to blind source separation using time-frequency distributions},
      journal = {IEEE Signal Processing Letters},
      month = {Mar.},
      year = {2004},
      volume = {11},
      number = {3},
      pages = {386-389},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/journals/ieee_spl_tfbss.pdf},
      doi = {https://doi.org/10.1109/LSP.2003.819343}
    }
    
    C. Févotte, S.J. Godsill & P.J. Wolfe Bayesian approach for blind separation of underdetermined mixtures of sparse sources 2004 Proc. 5th International Conference on Independent Component Analysis and Blind Source Separation (ICA)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{ica04,
      author = {C. Févotte and S. J. Godsill and P. J. Wolfe} ,
      title = {Bayesian approach for blind separation of underdetermined mixtures of sparse sources},
      booktitle = {Proc. 5th International Conference on Independent Component Analysis and Blind Source Separation (ICA)},
      address = {Granada, Spain},
      month = {Sep.},
      year = {2004},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/ica04.pdf}
    }
    
    D. Farina, F. Lebrun, C. Févotte, C. Doncarli & R. Merletti Blind source separation of linear mixtures of non-stationary surface EMG signals 2003 Proc. 19e colloque GRETSI sur le Traitement du Signal et des Images   inproceedings  
    BibTeX:
    
    @inproceedings{gretsi03-1,
      author = {D. Farina and F. Lebrun and C. Févotte and C. Doncarli and R. Merletti} ,
      title = {Blind source separation of linear mixtures of non-stationary surface EMG signals},
      booktitle = {Proc. 19e colloque GRETSI sur le Traitement du Signal et des Images},
      address = {Paris, France},
      month = {Sep.},
      year = {2003}
    }
    
    E. Vincent, C. Févotte & R. Gribonval Comment évaluer les algorithmes de séparation audio ? 2003 Proc. 19e colloque GRETSI sur le Traitement du Signal et des Images   inproceedings  
    BibTeX:
    
    @inproceedings{gretsi03-2,
      author = {E. Vincent and C. Févotte and R. Gribonval} ,
      title = {Comment évaluer les algorithmes de séparation audio ?},
      booktitle = {Proc. 19e colloque GRETSI sur le Traitement du Signal et des Images},
      address = {Paris, France},
      month = {Sep.},
      year = {2003}
    }
    
    C. Févotte, A. Debiolles & C. Doncarli Blind separation of FIR convolutive mixtures: application to speech signals 2003 Proc. 1st ISCA Workshop on Non-Linear Speech Processing   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{nolisp03,
      author = {C. Févotte and A. Debiolles and C. Doncarli} ,
      title = {Blind separation of FIR convolutive mixtures: application to speech signals},
      booktitle = {Proc. 1st ISCA Workshop on Non-Linear Speech Processing},
      address = {Le Croisic, France},
      month = {May},
      year = {2003},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/nolisp03.pdf}
    }
    
    C. Févotte & C. Doncarli A unified presentation of blind source separation methods for convolutive mixtures using block-diagonalization 2003 Proc. 4th Symposium on Independent Component Analysis and Blind Source Separation (ICA)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{ica03-1,
      author = {C. Févotte and C. Doncarli} ,
      title = {A unified presentation of blind source separation methods for convolutive mixtures using block-diagonalization},
      booktitle = {Proc. 4th Symposium on Independent Component Analysis and Blind Source Separation (ICA)},
      address = {Nara, Japan},
      month = {Apr.},
      year = {2003},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/ica03a.pdf}
    }
    
    E. Vincent, C. Févotte, R. Gribonval & al A tentative typology of audio source separation tasks 2003 Proc. 4th Symposium on Independent Component Analysis and Blind Source Separation (ICA)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{ica03-2,
      author = {E. Vincent and C. Févotte and R. Gribonval and al} ,
      title = {A tentative typology of audio source separation tasks},
      booktitle = {Proc. 4th Symposium on Independent Component Analysis and Blind Source Separation (ICA)},
      address = {Nara, Japan},
      month = {Apr.},
      year = {2003},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/ica03b.pdf}
    }
    
    R. Gribonval, L. Benaroya, E. Vincent & C. Févotte Proposals for performance measurement in source separation 2003 Proc. 4th Symposium on Independent Component Analysis and Blind Source Separation (ICA)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{ica03-3,
      author = {R. Gribonval and L. Benaroya and E. Vincent and C. Févotte} ,
      title = {Proposals for performance measurement in source separation},
      booktitle = {Proc. 4th Symposium on Independent Component Analysis and Blind Source Separation (ICA)},
      address = {Nara, Japan},
      month = {Apr.},
      year = {2003},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/ica03c.pdf}
    }
    
    C. Févotte Approche temps-fréquence pour la séparation aveugle de sources non-stationnaires (A time-frequency approach for blind separation of non-stationary sources) 2003 École Centrale de Nantes et Université de Nantes   phdthesis [PDF]  
    BibTeX:
    
    @phdthesis{these,
      author = {C. Févotte} ,
      title = {Approche temps-fréquence pour la séparation aveugle de sources non-stationnaires (A time-frequency approach for blind separation of non-stationary sources)},
      institution = {École Centrale de Nantes et Université de Nantes},
      month = {Oct.},
      year = {2003},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/theses/these_fevotte.pdf}
    }
    
    A. Holobar, C. Févotte, C. Doncarli & D. Zazula Single autoterms selection for blind source separation in time-frequenccy plane 2002 Proc. 11th European Signal Processing Conference (EUSIPCO)   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{eusipco02,
      author = {A. Holobar and C. Févotte and C. Doncarli and D. Zazula} ,
      title = {Single autoterms selection for blind source separation in time-frequenccy plane},
      booktitle = {Proc. 11th European Signal Processing Conference (EUSIPCO)},
      address = {Toulouse, France},
      month = {Sep.},
      year = {2002},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/eusipco02.pdf}
    }
    
    L. De Lathauwer, C. Févotte, B. De Moor & J. Vandewalle Jacobi algorithm for joint block diagonalization in blind identification 2002 Proc. 23rd Symposium on Information Theory in the Benelux   inproceedings  
    BibTeX:
    
    @inproceedings{info02,
      author = {L. De Lathauwer and C. Févotte and B. De Moor and J. Vandewalle} ,
      title = {Jacobi algorithm for joint block diagonalization in blind identification},
      booktitle = {Proc. 23rd Symposium on Information Theory in the Benelux},
      address = {Louvain-la-Neuve, Belgium},
      month = {May},
      year = {2002},
      pages = {155--162}
    }
    
    E. Le Carpentier & C. Févotte Séparation de sources autorégressives gaussiennes par maximum de vraisemblance et filtrage de Kalman 2001 Proc. 18e colloque GRETSI sur le Traitement du Signal et des Images   inproceedings [PDF]  
    BibTeX:
    
    @inproceedings{gretsi01,
      author = {E. Le Carpentier and C. Févotte} ,
      title = {Séparation de sources autorégressives gaussiennes par maximum de vraisemblance et filtrage de Kalman},
      booktitle = {Proc. 18e colloque GRETSI sur le Traitement du Signal et des Images},
      address = {Toulouse, France},
      month = {Sep.},
      year = {2001},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/proceedings/gretsi01.pdf}
    }
    
    C. Févotte Acoustique des salles: modélisation de l'environnement sonore (Room acoustics: identification of room transfer functions) 2000 École Centrale de Nantes   mastersthesis [PDF]  
    BibTeX:
    
    @mastersthesis{dea2,
      author = {C. Févotte} ,
      title = {Acoustique des salles: modélisation de l'environnement sonore (Room acoustics: identification of room transfer functions)},
      institution = {École Centrale de Nantes},
      year = {2000},
      url = {https://www.irit.fr/ Cedric.Fevotte/publications/theses/dea_fevotte.pdf}
    }
    

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