@InProceedings{8903092,
  author = {S. Rajbamshi and G. Tauböck and P. Balazs and L. D. Abreu},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Random Gabor Multipliers for Compressive Sensing: A Simulation Study},
  year = {2019},
  pages = {1-5},
  abstract = {In this paper, we analyze by means of simulations the applicability of random Gabor multipliers as compressive measurements. In particular, we consider signals that are sparse with respect to Fourier or Gabor dictionaries, i.e., signals that are sparse in frequency or time-frequency domains. This work is an extension of our earlier contribution, where we introduced random Gabor multipliers to compress signals that are sparse in time domain. As reconstruction technique we employ the well known ℓ1-minimization procedure. Finally, we evaluate the compression performance of random Gabor multipliers by applying them to a specific audio signal with inherent time-frequency sparsity. Our results highlight the strong potential of random Gabor multipliers for present and future real-world audio applications.},
  keywords = {audio signal processing;compressed sensing;Fourier analysis;Gabor filters;minimisation;random processes;time-frequency analysis;random Gabor multipliers;signal compression;compressive sensing;simulation study;compressive measurements;Gabor dictionaries;Fourierdictionaries;ℓ1-minimization procedure;audio signal;time-frequency domains;frequency domains;time domain;time-frequency sparsity;Sparse matrices;Time-frequency analysis;Dictionaries;Stochastic processes;Standards;Gaussian distribution;Numerical models;Compressive Sensing;Gabor Multiplier;Random Matrix;Dictionary;Audio},
  doi = {10.23919/EUSIPCO.2019.8903092},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533764.pdf},
}

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In particular, we consider signals that are sparse with respect to Fourier or Gabor dictionaries, i.e., signals that are sparse in frequency or time-frequency domains. This work is an extension of our earlier contribution, where we introduced random Gabor multipliers to compress signals that are sparse in time domain. As reconstruction technique we employ the well known ℓ1-minimization procedure. Finally, we evaluate the compression performance of random Gabor multipliers by applying them to a specific audio signal with inherent time-frequency sparsity. 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Abreu},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {Random Gabor Multipliers for Compressive Sensing: A Simulation Study},\n year = {2019},\n pages = {1-5},\n abstract = {In this paper, we analyze by means of simulations the applicability of random Gabor multipliers as compressive measurements. In particular, we consider signals that are sparse with respect to Fourier or Gabor dictionaries, i.e., signals that are sparse in frequency or time-frequency domains. This work is an extension of our earlier contribution, where we introduced random Gabor multipliers to compress signals that are sparse in time domain. As reconstruction technique we employ the well known ℓ1-minimization procedure. Finally, we evaluate the compression performance of random Gabor multipliers by applying them to a specific audio signal with inherent time-frequency sparsity. 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