Indexing Metadata

1 Title of the Article A New Residual Convolutional Neural Network-Based Speech Improvement
2 Author's name M. Balasubrahmanyam: Assistant Professor, Department of Electronics and Communication Engineering, PACE Institute of Technology and Sciences, Ongole, India
3 Author's name B. Haribabu, P. Uday Kumar
4 Subject Electronics and Communication Engineering
5 Keyword(s) Convolutional Neural Networks, Liver, Segmentation, Tumor, ResNet, Deep Learning.
6 Abstract

Among the most crucial methods for denoising a noisy voice signal and enhancing its quality is speech enhancement. This study makes use of Adaptive Residual Neural Network technique to reduces maximum off background noise. This method continuously monitors the background noise depends upon the environmental changes using SNR parameter. It has two functions first one is non linear functions followed by convolutional neural networks and second one is linearity followed due to Residual neural networks. By using these factors remove background noise even SNR is low conditions. Compared to other techniques this technique is new,fastest, requires less training and also reduces size.

 

7 Publisher Innovative Research Publication
8 Journal Name; vol., no. International Journal of Innovative Research in Computer Science & Technology (IJIRCST); Volume-10 Issue-6
9 Publication Date November 2022
10 Type Peer-reviewed Article
11 Format PDF
12 Uniform Resource Identifier https://ijircst.org/view_abstract.php?title=A-New-Residual-Convolutional-Neural-Network-Based-Speech-Improvement&year=2022&vol=10&primary=QVJULTEwNTE=
13 Digital Object Identifier(DOI) 10.55524/ijircst.2022.10.6.7   https://doi.org/10.55524/ijircst.2022.10.6.7
14 Language English
15 Page No 39-42