Determination of Optimum Parameters for Cochlear Implants Speech Processors by Using Objective Measures
Author:
Publisher
El-Cezeri: Journal of Science and Engineering
Subject
General Physics and Astronomy,General Engineering,General Chemical Engineering,General Chemistry,General Computer Science
Reference33 articles.
1. [1]. Dorman, M. F., Loizou, P. C., Fitzke, J., and Tu, Z., The recognition of sentences in noise by normal-hearing listeners using simulations of cochlear-implant signal processors with 6–20 channels, The Journal of the Acoustical Society of America, 1998, 104(6), 3583-3585.
2. [2]. Guo, R., Ma, X., Liao, M., Liu, Y., Hu, Y., Qian, X., and Tang, M., Development and application of cochlear implant-based electric-acoustic stimulation of spiral ganglion neurons. ACS Biomaterials Science & Engineering, 2019, 5(12), 6735-6741.
3. [3]. Wouters, J., McDermott, H. J., and Francart, T., Sound coding in cochlear implants: From electric pulses to hearing. IEEE Signal Processing Magazine, 2015, 32(2), 67-80.
4. [4]. Berg, K. A., Noble, J. H., Dawant, B. M., Dwyer, R. T., Labadie, R. F., and Gifford, R. H., Speech recognition with cochlear implants as a function of the number of channels: Effects of electrode placement. The Journal of the Acoustical Society of America, 2020, 147(5), 3646-3656.
5. [5]. Bratu, E., Dwyer, R., and Noble, J., A Graph-Based Method for Optimal Active Electrode Selection in Cochlear Implants. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020, (pp. 34-43). Springer, Cham.)
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