A New Residual Convolutional Neural Network-Based Speech Improvement

M. Balasubrahmanyam, B. Haribabu, P. Uday Kumar

doi:10.55524/ijircst.2022.10.6.7

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.

Keywords

Convolutional Neural Networks, Liver, Segmentation, Tumor, ResNet, Deep Learning.

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Cites this article as

[M. Balasubrahmanyam, B. Haribabu, P. Uday Kumar (2022), A New Residual Convolutional Neural Network-Based Speech Improvement, International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-10, Issue-6, Page No-39-42], (ISSN 2347 - 5552). www.ijircst.org

Corresponding Author

M. Balasubrahmanyam

Assistant Professor, Department of Electronics and Communication Engineering, PACE Institute of Technology and Sciences, Ongole, India

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