Taylor‐AMS features and deep convolutional neural network for converting nonaudible murmur to normal speech-Reference-Cited by-同舟云学术

Taylor‐AMS features and deep convolutional neural network for converting nonaudible murmur to normal speech

Published:2020-02-14 Issue:3 Volume:36 Page:940-963
ISSN:0824-7935
Container-title:Computational Intelligence
language:en
Short-container-title:Computational Intelligence

Author:

Rajesh Kumar T.¹^ORCID,Suresh G. R²,Kanaga Subaraja S.³,Karthikeyan C.¹

Affiliation:

1. Department of Computer Science and EngineeringKoneru Lakshmaiah Education Foundation Guntur India

2. Department of Biomedical EngineeringSt. Peter's Institute of Higher Education and Research Chennai India

3. Department of Computer Science and EngineeringEaswari Engineering College Chennai India

Publisher

Wiley

Subject

Artificial Intelligence,Computational Mathematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/coin.12281

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3. NakajimaY KashiokaH ShikanoK CampbellN. Non‐audible murmur recognition input interface using stethoscopic microphone attached to the skin. Paper presented at: Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP); 2003:708‐711.

4. Predicting F0 and voicing from NAM‐captured whispered speech;Tran VA;Proc Speech Prosody,2008

5. ElmahdyMS MorsyAA. Subvocal speech recognition via close‐talk microphone and surface electromyogram using deep learning. Paper presented at: Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS); 2017; Prague:165‐168.

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