Publication Cover
Cochlear Implants International
An Interdisciplinary Journal for Implantable Hearing Devices
Volume 23, 2022 - Issue 6
122
Views
0
CrossRef citations to date
0
Altmetric
Original articles

Parameter tuning of time-frequency masking algorithms for reverberant artifact removal within the cochlear implant stimulus

ORCID Icon, ORCID Icon & ORCID Icon

References

  • Anzalone, M.C., Calandruccio, L., Doherty, K.A., Carney, L.H. 2006. Determination of the potential benefit of time-frequency gain manipulation. Ear and Hearing, 27(5): 480–492. doi:10.1097/01.aud.0000233891.86809.df.
  • Blamey, P.J., Dowell, R.C., Tong, Y.C., Clark, G.M. 1984. An acoustic model of a multiple-channel cochlear implant. The Journal of the Acoustical Society of America, 76(1): 97–103. doi:10.1121/1.391012.
  • Brungart, D.S., Chang, P.S., Simpson, B.D., Wang, D. 2006. Isolating the energetic component of speech-on-speech masking with ideal time-frequency segregation. Journal of the Acoustical Society of America, 120(6): 4007–4018. doi:10.1121/1.2363929.
  • Chu, K. M., Collins, L. M., & Mainsah, B. O. (2021). Phoneme-based time-frequency mask estimation for reverberant speech enhancement for cochlear implant users. Conference on Implantable Auditory Prostheses, Online/virtual conference.
  • Dorman, M.F., Loizou, P.C., Rainey, D. 1997. Speech intelligibility as a function of the number of channels of stimulation for signal processors using sine-wave and noise-band outputs. The Journal of the Acoustical Society of America, 102: 2403–2411. doi:10.1121/1.419603.
  • Hazrati, O., Lee, J., Loizou, P.C. 2013. Blind binary masking for reverberation suppression in cochlear implants. The Journal of the Acoustical Society of America, 133(3): 1607–1614. doi:10.1121/1.4789891.
  • Hazrati, O., Loizou, P.C. 2012. Tackling the combined effects of reverberation and masking noise using ideal channel selection. The Journal of Speech, Language, and Hearing Research, 55(2): 500–510. doi:10.1044/1092-4388(2011/11-0073).
  • Hazrati, O., Loizou, P.C. 2013. Reverberation suppression in cochlear implants using a blind channel-selection strategy. The Journal of the Acoustical Society of America, 133(6): 4188–4196. doi:10.1121/1.4804313.
  • Hazrati, O., Omid Sadjadi, S., Loizou, P.C., Hansen, J.H.L. 2013. Simultaneous suppression of noise and reverberation in cochlear implants using a ratio masking strategy. The Journal of the Acoustical Society of America, 134(5): 3759–3765. doi:10.1121/1.4823839.
  • Healy, E., Delfarah, M., Johnson, E., Wang, D. 2019. A deep learning algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker and reverberation. The Journal of the Acoustical Society of America, 145(3): 1874–1874. doi:10.1121/1.5101778.
  • Jeub, M., Schafer, M., & Vary, P. (2009). A binaural room impulse response database for the evaluation of dereverberation algorithms. 16th International Conference on Digital Signal Processing (Vols 1 and 2, pp. 550–554), Santorini, Greece.
  • Kokkinakis, K., Hazrati, O., Loizou, P.C. 2011. A channel-selection criterion for suppressing reverberation in cochlear implants. The Journal of the Acoustical Society of America, 129(5): 3221–3232. doi:10.1121/1.3559683.
  • Kokkinakis, K., Stohl, J.S. 2021. Optimized gain functions in ideal time-frequency masks and their application to dereverberation for cochlear implants. Journal of the Acoustical Society of America, Express Letters, 1(8): 084401. doi:10.1121/10.0005740.
  • Koning, R., Madhu, N., Wouters, J. 2015. Ideal time–frequency masking algorithms lead to different speech intelligibility and quality in normal-hearing and cochlear implant listeners. IEEE Transactions on Biomedical Engineering, 62(1): 331–341. doi:10.1109/TBME.2014.2351854.
  • Kressner, A.A., Westermann, A., Buchholz, J.M. 2018. The impact of reverberation on speech intelligibility in cochlear implant recipients. The Journal of the Acoustical Society of America, 144(2): 1113–1122. doi:10.1121/1.5051640.
  • Kuttruff, H. 2009. Room acoustics. London: CRC Press.
  • Li, X., Li, J., & Yan, Y. (2017). Ideal ratio mask estimation using deep neural networks for monaural speech segregation in noisy reverberant conditions. Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH) (pp. 1203–1207), London. doi:10.21437/Interspeech.2017-549
  • Li, N., Loizou, P.C. 2008. Factors influencing intelligibility of ideal binary-masked speech: implications for noise reduction. The Journal of the Acoustical Society of America, 123(3): 1673–1682. doi:10.1121/1.2832617.
  • Li, Y., Wang, D.L. 2009. On the optimality of ideal binary time-frequency masks. Speech Communication, 51(3): 230–239. doi:10.1016/j.specom.2008.09.001.
  • Lim, J.S., Oppenheim, A.V. 1979. Enhancement and bandwidth compression of noisy speech. Proceedings of the IEEE, 67(12): 1586–1604. doi:10.1109/PROC.1979.11540.
  • Loizou, P.C. 2006. Speech processing in vocoder-centric cochlear implants. Advances in Oto-Rhino-Laryngology, 64: 109–143. doi:10.1159/000094648.
  • Loizou, P.C. 2007. Speech enhancement: theory and practice. Boca Raton, FL: CRC Press. doi:10.1201/9781420015836.
  • McDermott, H.J., McKay, C.M., Vandali, A.E. 1992. A new portable sound processor for the University of Melbourne/nucleus limited multielectrode cochlear implant. The Journal of the Acoustical Society of America, 91(6): 3367–3371. doi:10.1121/1.402826.
  • Nabelek, A.K., Letowski, T.R., Tucker, F.M. 1989. Reverberant overlap-masking and self-masking in consonant identification. The Journal of the Acoustical Society of America, 86(4): 1259–1265.
  • Naylor, P.A., Gaubitch, N.D. 2010. Speech dereverberation. London: Springer-Verlag. doi:10.1007/978-1-84996-056-4_1.
  • Neuman, A.C., Wroblewski, M., Hajicek, J., Rubinstein, A. 2010. Combined effects of noise and reverberation on speech recognition performance of normal-hearing children and adults. Ear and Hearing, 31(3): 336–344. doi:10.1097/AUD.0b013e3181d3d514.
  • Nilsson, M., Soli, S.D., Sullivan, J.A. 1994. Development of the hearing in noise test for the measurement of speech reception thresholds in quiet and in noise. The Journal of the Acoustical Society of America, 95(2): 1085–1099.
  • Roman, N., Woodruff, J. 2011. Intelligibility of reverberant noisy speech with ideal binary masking. The Journal of the Acoustical Society of America, 130(4): 2153–2161. doi:10.1121/1.3631668.
  • Roman, N., Woodruff, J. 2013. Speech intelligibility in reverberation with ideal binary masking: effects of early reflections and signal-to-noise ratio threshold. The Journal of the Acoustical Society of America, 133(3): 1707–1717. doi:10.1121/1.4789895.
  • Shahidi, L. K., Collins, L. M., & Mainsah, B. O. (2021). A comparison of real-time feasible mask estimation models for reverberant artifact removal in cochlear implant pulse trains. Conference on Implantable Auditory Prostheses, Online/virtual conference.
  • Strydom, T., Hanekom, J.J. 2011. The performance of different synthesis signals in acoustic models of cochlear implants. The Journal of the Acoustical Society of America, 129(2): 920–933. doi:10.1121/1.3518760.
  • Studebaker, G.A. 1985. A “rationalized” arcsine transform. Journal of Speech and Hearing Research, 28: 455–462.
  • Swanson, B., Mauch, H. 2006. E10511DD: Nucleus MATLAB Toolbox 4.20 Software User Manual (4.20). Article 4.20.
  • Wang, D. 2005. On ideal binary mask as the computational goal of auditory scene analysis. In: P. Divenyi, (ed.) Speech separation by humans and machines, p. 181–197. New York, NY: Springer US.
  • Wang, D. 2008. Time-frequency masking for speech separation and its potential for hearing aid design. Trends in Amplification, 12(4): 332–353. doi:10.1177/1084713808326455.
  • Wang, D., Kjems, U., Pedersen, M.S., Boldt, J.B., Lunner, T. 2009. Speech intelligibility in background noise with ideal binary time-frequency masking. Journal of the Acoustical Society of America, 125(4): 2336–2347. doi:10.1121/1.3083233.
  • Whitmal, N.A., Poissant, S.F., Freyman, R.L., Helfer, K.S. 2007. Speech intelligibility in cochlear implant simulations: effects of carrier type, interfering noise, and subject experience. The Journal of the Acoustical Society of America, 122(4): 2376–2388. doi:10.1121/1.2773993.
  • Zhao, Y., Wang, D., Johnson, E.M., Healy, E.W. 2018. A deep learning based segregation algorithm to increase speech intelligibility for hearing-impaired listeners in reverberant-noisy conditions. The Journal of the Acoustical Society of America, 144(3): 1627–1637. doi:10.1121/1.5055562.
  • Zhao, Y., Wang, D., Merks, I., & Zhang, T. (2016). DNN-based enhancement of noisy and reverberant speech. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6525–6529), Shanghai, China. doi:10.1109/ICASSP.2016.7472934
  • Zhao, Y., Wang, Z.-Q., & Wang, D. (2017). A two-stage algorithm for noisy and reverberant speech enhancement. International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 5580–5584), New Orleans, LA.
  • Zhao, Y., Xu, B., Giri, R., & Zhang, T. (2018). Perceptually guided speech enhancement using deep neural networks. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5074–5078), Calgary, AB.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.