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Computational intelligence for speech enhancement using deep neural network

Hepsiba D., Justin Judith

Computer Assisted Methods in Engineering and Science

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2022

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Vol. 29, no. 1-2 spec.

71--85

EN

In real time, the speech signal received contains noise produced in the background andreverberations. These disturbances reduce the quality of speech; therefore, it is importantto eliminate the noise and increase the intelligibility and quality of speech signal. Speechenhancement is the primary task in any real-time application that handles speech signals.In the proposed method, the most effective and challenging noise, i.e., babble noise, isremoved, and the clean speech is recovered. The enhancement of the corrupted speechsignal is done by applying a deep neural network-based denoising algorithm in which theideal ratio mask is used to mask the noisy speech and separate the clean speech signal.In the proposed system, the speech signal corrupted by noise is enhanced. Evaluation ofenhanced speech signal by performance metrics such as short time objective intelligibilityand signal to noise ratio of the denoised speech show that the speech intelligibility andspeech quality are improved by the proposed method.

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Automated lung nodule detection in CT images by optimized CNN: impact of improved whale optimization algorithm

Kumar M. Kiran, Amalanathan Anthoniraj

Computer Assisted Methods in Engineering and Science

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2022

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Vol. 29, no. 1-2 spec.

7--31

EN

Lung cancer is one of the leading causes of cancer-related deaths among individuals.It should be diagnosed at the early stages, otherwise it may lead to fatality due to itsmalicious nature. Early detection of the disease is very significant for patients’ survival, andit is a challenging issue. Therefore, a new model including the following stages: (1) imagepre-processing, (2) segmentation, (3) proposed feature extraction and (4) classificationis proposed. Initially, pre-processing takes place, where the input image undergoes specificpre-processing. The pre-processed images are then subjected to segmentation, which iscarried out using the Otsu thresholding model. The third phase is feature extraction, wherethe major contribution is obtained. Specifically, 4D global local binary pattern (LBP)features are extracted. After their extracting, the features are subjected to classification,where the optimized convolutional neural network (CNN) model is exploited. For a moreprecise detection of a lung nodule, the filter size of a convolution layer, hidden unit inthe fully connected layer and the activation function in CNN are tuned optimally byan improved whale optimization algorithm (WOA) called the whale with tri-level enhancedencircling behavior (WTEEB) model.

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Rotation Invariance in Graph Convolutional Networks

Mac Nguyen Anh, Nguyen Hung Son

Annals of Computer Science and Information Systems

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2021

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Vol. 25

81--90

EN

Convolution filters in deep convolutional networks display rotation variant behavior. While learned invariant behavior can be partially achieved, this paper shows that current methods of utilizing rotation variant features can be improved by proposing a grid-based graph convolutional network. We demonstrate that Grid-GCN heavily outperforms existing models on rotated images, and through a set of ablation studies, we show how the performance of Grid-GCN implies that there exist more performant methods to utilize fundamentally rotation variant features and we conclude that the inherit nature of spectral graph convolutions is able to learn invariant behavior.

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Detection of Arrhythmia using Neural Network

Mohapatra S. K., Palo H. K., Mohanty M. N.

Annals of Computer Science and Information Systems

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2018

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Vol. 14

97--100

EN

There is an increase in cardio logical patients all over the world due to change in modern life style. It forces the medical researchers to search for smart devices that can diagnosis and predict the onset of cardiac problem before it is too late. This motivates the authors to predict Arrhythmia that can help both the patients and the medical practitioners for better healthcare services. The proposed method uses the frequency domain information which can represent the ECG signals of Arrhythmia patients better. Features representing the MIT-BIH Arrhythmia are extracted using the efficient Short Time Fourier Transform and the Wavelet transform. A comparison of these features is made with that of normal human being using Neural Network based classifier. Wavelet based features has shown an improvement of Accuracy over that of STFT features in classifying Arrhythmia as our results reveal. A Mean Square Error (MSE) of with wavelet transform has validated our results.