@InProceedings{7362426,
  author = {C. Févotte and M. Kowalski},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Hybrid sparse and low-rank time-frequency signal decomposition},
  year = {2015},
  pages = {464-468},
  abstract = {We propose a new hybrid (or morphological) generative model that decomposes a signal into two (and possibly more) layers. Each layer is a linear combination of localised atoms from a time-frequency dictionary. One layer has a low-rank time-frequency structure while the other as a sparse structure. The time-frequency resolutions of the dictionaries describing each layer may be different. Our contribution builds on the recently introduced Low-Rank Time-Frequency Synthesis (LRTFS) model and proposes an iterative algorithm similar to the popular iterative shrinkage/thresholding algorithm. We illustrate the capacities of the proposed model and estimation procedure on a tonal + transient audio decomposition example.},
  keywords = {audio signal processing;signal resolution;time-frequency analysis;hybrid generative model;morphological generative model;low-rank time-frequency signal decomposition;sparse signal decomposition;time-frequency resolutions;low-rank time-frequency synthesis model;LRTFS model;iterative algorithm;tonal-transient audio decomposition;Time-frequency analysis;Dictionaries;Atomic layer deposition;Transient analysis;Estimation;Signal processing;Sparse matrices;Low-rank time-frequency synthesis;sparse component analysis;hybrid/morphological decompositions;non-negative matrix factorisation},
  doi = {10.1109/EUSIPCO.2015.7362426},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570100947.pdf},
}

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Kowalski},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Hybrid sparse and low-rank time-frequency signal decomposition},\n year = {2015},\n pages = {464-468},\n abstract = {We propose a new hybrid (or morphological) generative model that decomposes a signal into two (and possibly more) layers. Each layer is a linear combination of localised atoms from a time-frequency dictionary. One layer has a low-rank time-frequency structure while the other as a sparse structure. The time-frequency resolutions of the dictionaries describing each layer may be different. Our contribution builds on the recently introduced Low-Rank Time-Frequency Synthesis (LRTFS) model and proposes an iterative algorithm similar to the popular iterative shrinkage/thresholding algorithm. 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