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Attempts to develop speech enhancement algorithms with improved speech intelligibility for cochlear implant (CI) users have met with limited success.
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2012
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H. Ali, N. Mamun, A. Bruggeman, R. C. M. Chandra Shekar, J. N. Saba, and J. H. L. Hansen, “The cci-mobile vocoder,”
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Y. Xu, J. Du, L.-R. Dai, and C.-H. Lee, “A regression approach to speech enhancement based on deep neural networks,”
2015
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N. Mamun, W. A. Jassim, and M. S. Zilany, “Prediction of speech intelligibility using a neurogram orthogonal polynomial measure (nopm),”
2015
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S.-W. Fu, Y. Tsao, and X. Lu, “Snr-aware convolutional neural network modeling for speech enhancement.” in
2016
Cited alongside, same era.
T. Goehring, F. Bolner, J. J. Monaghan, B. van Dijk, A. Zarowski, and S. Bleeck, “Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users,”
2017
Cited alongside, same era.
L. Sun, J. Du, L.-R. Dai, and C.-H. Lee, “Multiple-target deep learning for lstm-rnn based speech enhancement,” in
2017
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S.-W. Fu, Y. Tsao, X. Lu, and H. Kawai, “Raw waveform-based speech enhancement by fully convolutional networks,” in
2017
Cited alongside, same era.
S. Khorram, Z. Aldeneh, D. Dimitriadis, M. McInnis, and E. M. Provost, “Capturing long-term temporal dependencies with convolutional networks for continuous emotion recognition,”
2017
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C. Lane, K. Zimmerman, S. Agrawal, and L. Parnes, “Cochlear implant failures and reimplantation: A 30-year analysis and literature review,”
2019
Closest in time.
A. Dieter, C. J. Duque-Afonso, V. Rankovic, M. Jeschke, and T. Moser, “Near physiological spectral selectivity of cochlear optogenetics,”
2019
Closest in time.
J. H. L. Hansen, H. Ali, J. Saba, R. C. shekhar, N. Mamun, R. Ghosh, and A. Brueggeman, “Cci-mobile: Design and evaluation of a cochlear implant and hearing aid research platform for speech scientists and engineers.”
2019
Closest in time.
N. Mamun, R. Ghose, and J. H. Hansen, “Quantifying cochlear implant users’ ability for speaker identification using ci auditory stimuli.” in
2019
Closest in time.
S. Khorram, M. McInnis, and E. M. Provost, “Jointly aligning and predicting continuous emotion annotations,”
2019
Closest in time.
M. Yousefi, S. Khorram, and J. H. L. Hansen, “Probabilistic permutation invariant training for speech separation,”
2019
Closest in time.
K. Akter and N. Mamun, “Predicting speech intelligibility with the regeneration of envelope from tfs cues for hearing impaired listeners,” in
2019
Closest in time.