@InProceedings{7362508,
  author = {E. Yilmaz and D. Baby and H. {Van hamme}},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Noise robust exemplar matching with coupled dictionaries for single-channel speech enhancement},
  year = {2015},
  pages = {874-878},
  abstract = {In this paper, we propose a single-channel speech enhancement system based on the noise robust exemplar matching (N-REM) framework using coupled dictionaries. N-REM approximates noisy speech segments as a sparse linear combination of speech and noise exemplars that are stored in multiple dictionaries based on their length and associated speech unit. The dictionaries providing the best approximation of the noisy mixtures are used to estimate the speech component. We further employ a coupled dictionary approach that performs the approximation in the lower dimensional mel domain to benefit from the reduced computational load and better generalization, and the enhancement in the short-time Fourier transform (STFT) domain for higher spectral resolution. The proposed enhancement system is shown to have superior performance compared to the exemplar-based sparse representations approach using fixed-length exemplars in a single overcomplete dictionary.},
  keywords = {Fourier transforms;speech enhancement;noise robust exemplar matching;single-channel speech enhancement;noisy speech segments;sparse linear combination;short-time Fourier transform;exemplar-based sparse representations;Speech;Dictionaries;Signal to noise ratio;Speech enhancement;Noise measurement;Approximation methods;Europe;speech enhancement;exemplar matching;coupled dictionaries;non-negative sparse coding},
  doi = {10.1109/EUSIPCO.2015.7362508},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570102149.pdf},
}

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N-REM approximates noisy speech segments as a sparse linear combination of speech and noise exemplars that are stored in multiple dictionaries based on their length and associated speech unit. The dictionaries providing the best approximation of the noisy mixtures are used to estimate the speech component. We further employ a coupled dictionary approach that performs the approximation in the lower dimensional mel domain to benefit from the reduced computational load and better generalization, and the enhancement in the short-time Fourier transform (STFT) domain for higher spectral resolution. 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Baby and H. {Van hamme}},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Noise robust exemplar matching with coupled dictionaries for single-channel speech enhancement},\n year = {2015},\n pages = {874-878},\n abstract = {In this paper, we propose a single-channel speech enhancement system based on the noise robust exemplar matching (N-REM) framework using coupled dictionaries. N-REM approximates noisy speech segments as a sparse linear combination of speech and noise exemplars that are stored in multiple dictionaries based on their length and associated speech unit. The dictionaries providing the best approximation of the noisy mixtures are used to estimate the speech component. We further employ a coupled dictionary approach that performs the approximation in the lower dimensional mel domain to benefit from the reduced computational load and better generalization, and the enhancement in the short-time Fourier transform (STFT) domain for higher spectral resolution. 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