A speech denoising demonstration system using multi-model deep-learning neural networks
Author:
Funder
National Science and Technology Council
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17655-1.pdf
Reference35 articles.
1. Emani RPK, Telagathoti P, Prasad N (2020) Telephony speech enhancement for elderly people. Proc Int Conf Comput Commun Signal Process (ICCCSP), Chennai, India; 1–4. https://doi.org/10.1109/ICCCSP49186.2020.9315269
2. Prasad N, Praveen Kumar E, Sitaramanjaneyulu P, Srinivasa Raju GRLVN (2020) Telephony speech enhancement for hearing-impaired people. Proc Int Conf Comput Commun Security (ICCCS), Patna, India, 2020;1-4https://doi.org/10.1109/ICCCS49678.2020.9277386
3. Koning R, Bruce IC, Denys S, Wouters J (2018) Perceptual and model-based evaluation of ideal time-frequency noise reduction in hearing-impaired listeners. IEEE Trans Neural Syst Rehabilitation Eng 26:687–697. https://doi.org/10.1109/TNSRE.2018.2794557
4. Kavalekalam MS, Nielsen JK, Boldt JB, Christensen MG (2019) Model-based speech enhancement for intelligibility improvement in binaural hearing aids. IEEE/ACM Trans Audio Speech Language Process 27:99–113. https://doi.org/10.1109/TASLP.2018.2872128
5. Islam MSA, Mahmud THA, Khan WU, Ye Z (2020) Supervised single channel speech enhancement based on stationary wavelet transforms and non-negative matrix factorization with concatenated framing process and subband smooth ratio mask. J Signal Process Syst 92:445–458. https://doi.org/10.1007/s11265-019-01480-7
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