| Author | Title | Year | Journal/Proceedings/Institution | BibTeX type | URL |
|---|---|---|---|---|---|
| V.Y.F. Tan & C. Févotte | Automatic relevance determination in nonnegative matrix factorization with the beta-divergence | in press | IEEE Transactions on Pattern Analysis and Machine Intelligence | article | [PDF] |
| 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},
year = {in press},
url = {http://arxiv.org/abs/1111.6085}
}
|
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| N. Seichepine, S. Essid, C. Févotte & O. Cappé | Soft nonnegative matrix co-factorization with application to multimodal speaker diarization [BibTeX] |
2013 | Proc.~IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) | inproceedings | |
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}
}
|
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| S. Essid & 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 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 = {http://www.unice.fr/cfevotte/publications/journals/ieee_multimedia_smoothnmf.pdf},
doi = {http://dx.doi.org/10.1109/TMM.2012.2228474}
}
|
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| A. Lefèvre, F. Bach & C. Févotte | Semi-supervised NMF with time-frequency annotations for single-channel source separation [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/ismir12a.pdf}
}
|
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| T. Gerber, M. Dutasta, L. Girin & C. Févotte | Professionally-produced music separation guided by covers [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/ismir12b.pdf}
}
|
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| 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 = {http://www.unice.fr/cfevotte/publications/journals/ieee_sp_mmle.pdf},
doi = {http://dx.doi.org/10.1109/TSP.2012.2207117}
}
|
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| S. Essid & C. Févotte | Decomposing the video editing structure of a talk-show using nonnegative matrix factorization [BibTeX] |
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}
}
|
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| B. King, C. Févotte & P. Smaragdis | Optimal cost function and magnitude power for NMF-based speech separation and music interpolation [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/mlsp12.pdf}
}
|
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| M. Zetlaoui, M. Feinberg, P. Verger & S. Clémençon | Extraction of Food Consumption Systems by Nonnegative Matrix Factorization (NMF) for the Assessment of Food Choices [BibTeX] |
2011 | Biometrics | article | [PDF] [DOI] |
BibTeX:
@article{zet11,
author = {Zetlaoui, M. and Feinberg, M. and Verger, P. and Clémençon, S.} ,
title = {Extraction of Food Consumption Systems by Nonnegative Matrix Factorization (NMF) for the Assessment of Food Choices},
journal = {Biometrics},
publisher = {Blackwell Publishing Inc},
year = {2011},
url = {http://dx.doi.org/10.1111/j.1541-0420.2011.01588.x},
doi = {http://dx.doi.org/10.1111/j.1541-0420.2011.01588.x}
}
|
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| O. Dikmen & C. Févotte | Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation | 2011 | Advances in Neural Information Processing Systems 24 (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 24 (NIPS)},
editor = {J. Shawe-Taylor and R.S. Zemel and P. Bartlett and F.C.N. Pereira and K.Q. Weinberger},
publisher = {MIT Press},
address = {Granada, Spain},
month = {Dec.},
year = {2011},
pages = {2267--2275},
url = {http://www.unice.fr/cfevotte/publications/proceedings/nips11.pdf}
}
|
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| V.Y.F. Tan & C. Févotte | Automatic relevance determination in nonnegative matrix factorization with the beta-divergence [BibTeX] |
2011 | 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 = {NIPS workshop on Sparse Representation and Low-rank Approximation},
address = {Sierra Nevada, Spain},
month = {Dec.},
year = {2011},
url = {http://www.unice.fr/cfevotte/publications/proceedings/nips11_workshop.pdf}
}
|
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| 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 = {http://www.unice.fr/cfevotte/publications/journals/ieee_asl_probachord.pdf},
doi = {http://dx.doi.org/10.1109/TASL.2010.2098870}
}
|
|||||
| A. Lefèvre, F. Bach & C. Févotte | Online algorithms for nonnegative matrix factorization with the Itakura-Saito divergence [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/waspaa11.pdf}
}
|
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| 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 = {http://www.unice.fr/cfevotte/publications/journals/neco11.pdf},
doi = {http://dx.doi.org/10.1162/NECO_a_00168}
}
|
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| C. Févotte & J. Idier | Algorithmes de factorisation en matrices non-négatives fondée sur la beta-divergence [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/gretsi11a.pdf}
}
|
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| O. Cappé, C. Févotte & D. Rhodes | Algorithme EM en ligne simulé pour la factorisation non-négative probabiliste [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/gretsi11b.pdf}
}
|
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| A. Lefèvre, F. Bach & C. Févotte | Factorisation de matrices structurée en groupes avec la divergence d'Itakura-Saito [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/gretsi11c.pdf}
}
|
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| C. Févotte, O. Cappé & A.T. Cemgil | Efficient Markov chain Monte Carlo inference in composite models with space alternating data augmentation [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/ssp11.pdf}
}
|
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| C. Févotte | Majorization-minimization algorithm for smooth Itakura-Saito nonnegative matrix factorization [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/icassp11a.pdf}
}
|
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| O. Dikmen & C. Févotte | Maximum marginal likelihood estimation for nonnegative dictionary learning [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/icassp11b.pdf}
}
|
|||||
| A. Lefèvre, F. Bach & C. Févotte | Itakura-Saito nonnegative matrix factorization with group sparsity [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/icassp11c.pdf}
}
|
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| A. Ozerov, C. Févotte, R. Blouet & J.-L. Durrieu | Multichannel nonnegative tensor factorization with structured constraints for user-guided audio source separation [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/icassp11d.pdf}
}
|
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| A. Ozerov, C. Févotte & R. Blouet | Automatic source separation via joint use of segmental information and spatial diversity [BibTeX] |
2011 | patent | ||
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.}
}
|
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| 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.},
year = {2010},
volume = {6684},
pages = {102-115},
note = {Long paper.},
url = {http://www.unice.fr/cfevotte/publications/proceedings/cmmr10.pdf}
}
|
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| H. Lantéri, C. Theys, C. Richard & C. Févotte | Split gradient method for nonnegative matrix factorization [BibTeX] |
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 = {http://www.unice.fr/cfevotte/publications/proceedings/eusipco10.pdf}
}
|
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